diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation_20251017_081656.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation_20251017_081656.log new file mode 100644 index 0000000000000000000000000000000000000000..a22698cb8d862f515dc2ff0fc262a15aa2830e93 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation_20251017_081656.log @@ -0,0 +1,2314 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation_20251017_081656.log +Timestamp: 2025-10-17 08:16:56 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`. + 0it [00:00, ?it/s] 0it [00:00, ?it/s] +[2025-10-17 08:16:59,103] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:02,175] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 08:17:02,177] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 1.0 --temperature_attn_text 1.7 --temperature_mlp_text 1.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 1.0 --temperature_attn_vision 1.7 --temperature_mlp_vision 1.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 1.0 --temperature_connector 1.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 08:17:04,715] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:05,784] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 08:17:05,784] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 08:17:05,784] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 08:17:05,784] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 08:17:05,784] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 08:17:05,784] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 08:17:05,784] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 08:17:05,786] [INFO] [launch.py:253:main] process 389436 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,788] [INFO] [launch.py:253:main] process 389437 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,790] [INFO] [launch.py:253:main] process 389438 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,792] [INFO] [launch.py:253:main] process 389439 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,794] [INFO] [launch.py:253:main] process 389440 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,796] [INFO] [launch.py:253:main] process 389441 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,798] [INFO] [launch.py:253:main] process 389442 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:17:05,800] [INFO] [launch.py:253:main] process 389443 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 08:17:12,510] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,612] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,700] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,734] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,760] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,760] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,786] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,799] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:17:12,979] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,015] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,117] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,156] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,187] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,188] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,208] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,223] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:17:13,223] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.7, 'temperature_mlp': 1.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.7, + "temperature_mlp": 1.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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multicast support is not available on dev 7 +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO comm 0x5632f9188fb0 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO comm 0x55effa73bf60 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO comm 0x5587510cf170 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO comm 0x561c6d51a3b0 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO comm 0x556bf16f7f80 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO comm 0x5584662e97e0 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO comm 0x55638d892290 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 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[10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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peer +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389438:391133 [2] NCCL INFO ncclCommInitRank comm 0x561c6d51a3b0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389439:391134 [3] NCCL INFO ncclCommInitRank comm 0x557816c1f4f0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389436:391107 [0] NCCL INFO ncclCommInitRank comm 0x556bf16f7f80 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389437:391130 [1] NCCL INFO ncclCommInitRank comm 0x5632f9188fb0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:389442:391128 [6] NCCL INFO ncclCommInitRank comm 0x5587510cf170 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389443:391131 [7] NCCL INFO ncclCommInitRank comm 0x55effa73bf60 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389441:391132 [5] NCCL INFO ncclCommInitRank comm 0x5584662e97e0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xf1914ed08b836334 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389440:391129 [4] NCCL INFO ncclCommInitRank comm 0x55638d892290 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xf1914ed08b836334 - Init COMPLETE +[2025-10-17 08:18:04,582] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 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'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 08:18:08,100] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin...Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... + +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=1.000000 +Pre-training init connector._connector.0.scores: Mean=1.000005 +Pre-training init connector._connector.2.scores: Mean=0.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 08:18:26,213 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 08:18:26,221 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:389438:396211 [2] NCCL INFO ncclCommInitRank comm 0x7fd0e006aa10 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389436:396207 [0] NCCL INFO ncclCommInitRank comm 0x7f2fc406b090 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389442:396212 [6] NCCL INFO ncclCommInitRank comm 0x7f028c06ac80 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389440:396209 [4] NCCL INFO ncclCommInitRank comm 0x7f861806aa10 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389443:396214 [7] NCCL INFO ncclCommInitRank comm 0x7ff35006aa50 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389439:396213 [3] NCCL INFO ncclCommInitRank comm 0x7f194406b450 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389441:396208 [5] NCCL INFO ncclCommInitRank comm 0x7f5aa806b3c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x62f1583f10b6ec81 - Init COMPLETE +ywang29-vrdb-test1-worker-0:389437:396210 [1] NCCL INFO ncclCommInitRank comm 0x7f0b2406a9d0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x62f1583f10b6ec81 - Init COMPLETE + 0%| | 1/520 [00:15<2:14:03, 15.50s/it] {'loss': 7.8159, 'grad_norm': 0.43962132839848433, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:15<2:14:03, 15.50s/it] 0%| | 2/520 [00:19<1:13:25, 8.50s/it] {'loss': 7.0813, 'grad_norm': 0.4565264970706716, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:19<1:13:25, 8.50s/it] 1%| | 3/520 [00:22<54:03, 6.27s/it] {'loss': 6.5681, 'grad_norm': 0.26534792031522303, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<54:03, 6.27s/it] 1%| | 4/520 [00:26<44:50, 5.22s/it] {'loss': 4.9783, 'grad_norm': 0.15340078514641156, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:26<44:50, 5.22s/it] 1%| | 5/520 [00:29<39:47, 4.64s/it] {'loss': 4.5958, 'grad_norm': 0.3382030787111079, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<39:47, 4.64s/it] 1%| | 6/520 [00:33<36:55, 4.31s/it] {'loss': 4.9649, 'grad_norm': 0.1299450415597302, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<36:55, 4.31s/it] 1%|▏ | 7/520 [00:37<34:47, 4.07s/it] {'loss': 3.4079, 'grad_norm': 0.07749363911485645, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:37<34:47, 4.07s/it] 2%|▏ | 8/520 [00:41<35:05, 4.11s/it] {'loss': 3.1923, 'grad_norm': 0.19758240155532028, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:41<35:05, 4.11s/it] 2%|▏ | 9/520 [00:44<33:37, 3.95s/it] {'loss': 2.8155, 'grad_norm': 0.03460659624842538, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<33:37, 3.95s/it] 2%|▏ | 10/520 [00:48<32:35, 3.83s/it] {'loss': 2.3833, 'grad_norm': 0.029506617377956578, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<32:35, 3.83s/it] 2%|▏ | 11/520 [00:52<32:21, 3.81s/it] {'loss': 2.357, 'grad_norm': 0.02111688152455683, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:52<32:21, 3.81s/it] 2%|▏ | 12/520 [00:55<31:38, 3.74s/it] {'loss': 2.6349, 'grad_norm': 0.027909163184843155, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<31:38, 3.74s/it][2025-10-17 08:19:30,718] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. 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0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:14<30:45, 3.67s/it] 3%|▎ | 18/520 [01:18<30:31, 3.65s/it] {'loss': 2.4645, 'grad_norm': 0.08427472686802188, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:18<30:31, 3.65s/it] 4%|▎ | 19/520 [01:21<30:16, 3.63s/it] {'loss': 4.6016, 'grad_norm': 0.2904409651880071, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:21<30:16, 3.63s/it] 4%|▍ | 20/520 [01:25<30:04, 3.61s/it] {'loss': 2.7929, 'grad_norm': 0.07117107062913017, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:25<30:04, 3.61s/it] 4%|▍ | 21/520 [01:28<30:06, 3.62s/it] {'loss': 3.2124, 'grad_norm': 0.05300705803334577, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:28<30:06, 3.62s/it] 4%|▍ | 22/520 [01:32<29:56, 3.61s/it] {'loss': 2.5256, 'grad_norm': 0.039699136066479956, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:32<29:56, 3.61s/it] 4%|▍ | 23/520 [01:36<29:46, 3.60s/it] {'loss': 2.2677, 'grad_norm': 0.017606880929474766, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:36<29:46, 3.60s/it] 5%|▍ | 24/520 [01:39<29:42, 3.59s/it] {'loss': 2.6303, 'grad_norm': 0.026558842416946657, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:39<29:42, 3.59s/it] 5%|▍ | 25/520 [01:43<29:42, 3.60s/it] {'loss': 2.3081, 'grad_norm': 0.02563381268462812, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:43<29:42, 3.60s/it] 5%|▌ | 26/520 [01:46<29:35, 3.59s/it] {'loss': 2.2071, 'grad_norm': 0.013209202488013532, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:46<29:35, 3.59s/it] 5%|▌ | 27/520 [01:50<29:26, 3.58s/it] {'loss': 2.0107, 'grad_norm': 0.017399859578397278, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:50<29:26, 3.58s/it] 5%|▌ | 28/520 [01:54<29:27, 3.59s/it] {'loss': 1.9714, 'grad_norm': 0.010676025029797469, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:54<29:27, 3.59s/it] 6%|▌ | 29/520 [01:57<29:31, 3.61s/it] {'loss': 1.9705, 'grad_norm': 0.015350226061628867, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:57<29:31, 3.61s/it] 6%|▌ | 30/520 [02:01<29:31, 3.61s/it] {'loss': 2.5243, 'grad_norm': 0.01671849980026423, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:01<29:31, 3.61s/it] 6%|▌ | 31/520 [02:04<29:34, 3.63s/it] {'loss': 1.9463, 'grad_norm': 0.009793921347140364, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:05<29:34, 3.63s/it] 6%|▌ | 32/520 [02:08<29:30, 3.63s/it] {'loss': 2.781, 'grad_norm': 0.04250056185239384, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:08<29:30, 3.63s/it] 6%|▋ | 33/520 [02:12<29:43, 3.66s/it] {'loss': 1.9901, 'grad_norm': 0.02342504110070967, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:12<29:43, 3.66s/it] 7%|▋ | 34/520 [02:16<30:06, 3.72s/it] {'loss': 1.9084, 'grad_norm': 0.008516983738821215, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:16<30:06, 3.72s/it] 7%|▋ | 35/520 [02:20<30:18, 3.75s/it] {'loss': 1.9376, 'grad_norm': 0.013513648575825075, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:20<30:18, 3.75s/it] 7%|▋ | 36/520 [02:23<30:26, 3.77s/it] {'loss': 2.0509, 'grad_norm': 0.014137521727245587, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:23<30:26, 3.77s/it] 7%|▋ | 37/520 [02:27<30:25, 3.78s/it] {'loss': 2.3764, 'grad_norm': 0.02335657031196121, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:27<30:25, 3.78s/it] 7%|▋ | 38/520 [02:31<30:22, 3.78s/it] {'loss': 2.0886, 'grad_norm': 0.020772001259702376, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:31<30:22, 3.78s/it] 8%|▊ | 39/520 [02:35<30:18, 3.78s/it] {'loss': 1.9498, 'grad_norm': 0.007049709090839739, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:35<30:18, 3.78s/it] 8%|▊ | 40/520 [02:39<30:16, 3.79s/it] {'loss': 1.8843, 'grad_norm': 0.016449487690420556, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:39<30:16, 3.79s/it] 8%|▊ | 41/520 [02:42<30:16, 3.79s/it] {'loss': 1.8508, 'grad_norm': 0.00966195934277566, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:42<30:16, 3.79s/it] 8%|▊ | 42/520 [02:46<30:14, 3.80s/it] {'loss': 1.9913, 'grad_norm': 0.009838489709262746, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:46<30:14, 3.80s/it] 8%|▊ | 43/520 [02:50<30:02, 3.78s/it] {'loss': 2.0994, 'grad_norm': 0.015211827148146388, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:50<30:02, 3.78s/it] 8%|▊ | 44/520 [02:54<29:56, 3.77s/it] {'loss': 2.1957, 'grad_norm': 0.008877769806496752, 'learning_rate': 0.19848077530122082, 'epoch': 0.08} + 8%|▊ | 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'grad_norm': 0.007089011209416157, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:16<29:28, 3.76s/it] 10%|▉ | 51/520 [03:20<29:21, 3.76s/it] {'loss': 1.679, 'grad_norm': 0.006034570951852243, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:20<29:21, 3.76s/it] 10%|█ | 52/520 [03:24<29:17, 3.76s/it] {'loss': 1.8468, 'grad_norm': 0.006985484724489631, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:24<29:17, 3.76s/it] 10%|█ | 53/520 [03:27<29:11, 3.75s/it] {'loss': 1.8219, 'grad_norm': 0.0077857638590927165, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:27<29:11, 3.75s/it] 10%|█ | 54/520 [03:31<29:40, 3.82s/it] {'loss': 1.6618, 'grad_norm': 0.005424431466869681, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:31<29:40, 3.82s/it] 11%|█ | 55/520 [03:35<30:05, 3.88s/it] {'loss': 1.6498, 'grad_norm': 0.006775142447487382, 'learning_rate': 0.1970596567453391, 'epoch': 0.11} + 11%|█ | 55/520 [03:35<30:05, 3.88s/it] 11%|█ | 56/520 [03:39<30:22, 3.93s/it] {'loss': 1.8158, 'grad_norm': 0.006855723481981793, 'learning_rate': 0.1969077286229078, 'epoch': 0.11} + 11%|█ | 56/520 [03:39<30:22, 3.93s/it] 11%|█ | 57/520 [03:44<30:32, 3.96s/it] {'loss': 1.6403, 'grad_norm': 0.006728639141520153, 'learning_rate': 0.19675203523431964, 'epoch': 0.11} + 11%|█ | 57/520 [03:44<30:32, 3.96s/it] 11%|█ | 58/520 [03:48<30:40, 3.98s/it] {'loss': 1.7833, 'grad_norm': 0.005144455211432859, 'learning_rate': 0.19659258262890683, 'epoch': 0.11} + 11%|█ | 58/520 [03:48<30:40, 3.98s/it] 11%|█▏ | 59/520 [03:52<30:47, 4.01s/it] {'loss': 1.8395, 'grad_norm': 0.008371659863437526, 'learning_rate': 0.19642937700206278, 'epoch': 0.11} + 11%|█▏ | 59/520 [03:52<30:47, 4.01s/it] 12%|█▏ | 60/520 [03:56<30:27, 3.97s/it] {'loss': 1.7146, 'grad_norm': 0.007172251875338679, 'learning_rate': 0.19626242469500121, 'epoch': 0.12} + 12%|█▏ | 60/520 [03:56<30:27, 3.97s/it] 12%|█▏ | 61/520 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65%|██████▌ | 339/520 [21:10<11:25, 3.79s/it] 65%|██████▌ | 340/520 [21:14<11:21, 3.79s/it] {'loss': 1.2505, 'grad_norm': 0.004142944351678658, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:14<11:21, 3.79s/it] 66%|██████▌ | 341/520 [21:18<11:15, 3.78s/it] {'loss': 1.277, 'grad_norm': 0.004273001392782499, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:18<11:15, 3.78s/it] 66%|██████▌ | 342/520 [21:21<11:02, 3.72s/it] {'loss': 1.4282, 'grad_norm': 0.004890174107219532, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:22<11:02, 3.72s/it] 66%|██████▌ | 343/520 [21:25<10:55, 3.71s/it] {'loss': 1.3933, 'grad_norm': 0.00465483001229315, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:25<10:55, 3.71s/it] 66%|██████▌ | 344/520 [21:29<10:48, 3.68s/it] {'loss': 1.2218, 'grad_norm': 0.003975560170268734, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:29<10:48, 3.68s/it] 66%|██████▋ | 345/520 [21:32<10:41, 3.67s/it] {'loss': 1.3484, 'grad_norm': 0.004520470968366567, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:32<10:41, 3.67s/it] 67%|██████▋ | 346/520 [21:36<10:35, 3.65s/it] {'loss': 1.3758, 'grad_norm': 0.004375057258916997, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:36<10:35, 3.65s/it] 67%|██████▋ | 347/520 [21:40<10:28, 3.63s/it] {'loss': 1.2463, 'grad_norm': 0.003909849226708089, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:40<10:28, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:43<10:22, 3.62s/it] {'loss': 1.2027, 'grad_norm': 0.004728265597326192, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:43<10:22, 3.62s/it] 67%|██████▋ | 349/520 [21:47<10:19, 3.62s/it] {'loss': 1.255, 'grad_norm': 0.004454076142100732, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:47<10:19, 3.62s/it] 67%|██████▋ | 350/520 [21:50<10:13, 3.61s/it] {'loss': 1.2805, 'grad_norm': 0.004475124925757971, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:50<10:13, 3.61s/it] 68%|██████▊ | 351/520 [21:54<10:12, 3.63s/it] {'loss': 1.1869, 'grad_norm': 0.003964419403654661, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:54<10:12, 3.63s/it] 68%|██████▊ | 352/520 [21:58<10:07, 3.61s/it] {'loss': 1.3151, 'grad_norm': 0.004000264982003876, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:58<10:07, 3.61s/it] 68%|██████▊ | 353/520 [22:01<10:04, 3.62s/it] {'loss': 1.3195, 'grad_norm': 0.003737847077837895, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:01<10:04, 3.62s/it] 68%|██████▊ | 354/520 [22:05<10:00, 3.62s/it] {'loss': 1.4626, 'grad_norm': 0.004370826907455206, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:05<10:00, 3.62s/it] 68%|██████▊ | 355/520 [22:09<09:55, 3.61s/it] {'loss': 1.2531, 'grad_norm': 0.003991108589508579, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:09<09:55, 3.61s/it] 68%|██████▊ | 356/520 [22:12<09:51, 3.61s/it] {'loss': 1.2502, 'grad_norm': 0.004017476931403349, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:12<09:51, 3.61s/it] 69%|██████▊ | 357/520 [22:16<09:47, 3.60s/it] {'loss': 1.2686, 'grad_norm': 0.0035983509929755284, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:16<09:47, 3.60s/it] 69%|██████▉ | 358/520 [22:19<09:43, 3.60s/it] {'loss': 1.2043, 'grad_norm': 0.004011748365752871, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:19<09:43, 3.60s/it] 69%|██████▉ | 359/520 [22:23<09:40, 3.61s/it] {'loss': 1.3817, 'grad_norm': 0.0044624363898576715, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:23<09:40, 3.61s/it] 69%|██████▉ | 360/520 [22:26<09:35, 3.60s/it] {'loss': 1.4085, 'grad_norm': 0.005344574436184053, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:27<09:35, 3.60s/it] 69%|██████▉ | 361/520 [22:30<09:33, 3.61s/it] {'loss': 1.3928, 'grad_norm': 0.004039467262017942, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:30<09:33, 3.61s/it] 70%|██████▉ | 362/520 [22:34<09:29, 3.60s/it] {'loss': 1.2693, 'grad_norm': 0.004361818655059272, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:34<09:29, 3.60s/it] 70%|██████▉ | 363/520 [22:37<09:26, 3.61s/it] {'loss': 1.3009, 'grad_norm': 0.0040109714384689585, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:37<09:26, 3.61s/it] 70%|███████ | 364/520 [22:41<09:23, 3.61s/it] {'loss': 1.4058, 'grad_norm': 0.0041728376345650025, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:41<09:23, 3.61s/it] 70%|███████ | 365/520 [22:45<09:19, 3.61s/it] {'loss': 1.3641, 'grad_norm': 0.004282868380076848, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:45<09:19, 3.61s/it] 70%|███████ | 366/520 [22:48<09:16, 3.61s/it] {'loss': 1.3109, 'grad_norm': 0.003926864120183499, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:48<09:16, 3.61s/it] 71%|███████ | 367/520 [22:52<09:17, 3.64s/it] {'loss': 1.3143, 'grad_norm': 0.003979261584259288, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:52<09:17, 3.64s/it] 71%|███████ | 368/520 [22:56<09:13, 3.64s/it] {'loss': 1.1646, 'grad_norm': 0.004421281737764511, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:56<09:13, 3.64s/it] 71%|███████ | 369/520 [22:59<09:09, 3.64s/it] {'loss': 1.3621, 'grad_norm': 0.004385249875479428, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:59<09:09, 3.64s/it] 71%|███████ | 370/520 [23:03<09:06, 3.64s/it] {'loss': 1.2177, 'grad_norm': 0.0036803497737187825, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:03<09:06, 3.64s/it] 71%|███████▏ | 371/520 [23:06<09:00, 3.63s/it] {'loss': 1.215, 'grad_norm': 0.004146459162710883, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:06<09:00, 3.63s/it] 72%|███████▏ | 372/520 [23:10<08:58, 3.64s/it] {'loss': 1.46, 'grad_norm': 0.0038958662345668416, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:10<08:58, 3.64s/it] 72%|███████▏ | 373/520 [23:14<08:54, 3.64s/it] {'loss': 1.3312, 'grad_norm': 0.004525706798703796, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:14<08:54, 3.64s/it] 72%|███████▏ | 374/520 [23:17<08:49, 3.63s/it] {'loss': 1.2991, 'grad_norm': 0.004196672738286894, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:17<08:49, 3.63s/it] 72%|███████▏ | 375/520 [23:21<08:48, 3.64s/it] {'loss': 1.2072, 'grad_norm': 0.0038652167667370574, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:21<08:48, 3.64s/it] 72%|███████▏ | 376/520 [23:25<08:44, 3.64s/it] {'loss': 1.3295, 'grad_norm': 0.003827130692066178, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:25<08:44, 3.64s/it] 72%|███████▎ | 377/520 [23:28<08:39, 3.63s/it] {'loss': 1.2701, 'grad_norm': 0.007301757829963305, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:28<08:39, 3.63s/it] 73%|███████▎ | 378/520 [23:32<08:36, 3.64s/it] {'loss': 1.3215, 'grad_norm': 0.003945477387280622, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:32<08:36, 3.64s/it] 73%|███████▎ | 379/520 [23:35<08:31, 3.63s/it] {'loss': 1.3061, 'grad_norm': 0.003928540009880384, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:36<08:31, 3.63s/it] 73%|███████▎ | 380/520 [23:39<08:28, 3.63s/it] {'loss': 1.4423, 'grad_norm': 0.0053992479123445335, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:39<08:28, 3.63s/it] 73%|███████▎ | 381/520 [23:43<08:25, 3.64s/it] {'loss': 1.2957, 'grad_norm': 0.004062662921463541, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:43<08:25, 3.64s/it] 73%|███████▎ | 382/520 [23:46<08:22, 3.64s/it] {'loss': 1.3756, 'grad_norm': 0.004207519927919408, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:46<08:22, 3.64s/it] 74%|███████▎ | 383/520 [23:50<08:18, 3.64s/it] {'loss': 1.1317, 'grad_norm': 0.004526323637346153, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:50<08:18, 3.64s/it] 74%|███████▍ | 384/520 [23:54<08:13, 3.63s/it] {'loss': 1.4992, 'grad_norm': 0.004473271335535201, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:54<08:13, 3.63s/it] 74%|███████▍ | 385/520 [23:57<08:10, 3.63s/it] {'loss': 1.2748, 'grad_norm': 0.0037578159533615324, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:57<08:10, 3.63s/it] 74%|███████▍ | 386/520 [24:01<08:06, 3.63s/it] {'loss': 1.2257, 'grad_norm': 0.003601830206175373, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:01<08:06, 3.63s/it] 74%|███████▍ | 387/520 [24:05<08:04, 3.64s/it] {'loss': 1.4613, 'grad_norm': 0.004239088032869379, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:05<08:04, 3.64s/it] 75%|███████▍ | 388/520 [24:08<08:01, 3.64s/it] {'loss': 1.1697, 'grad_norm': 0.0037790262720102173, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:08<08:01, 3.64s/it] 75%|███████▍ | 389/520 [24:12<07:57, 3.64s/it] {'loss': 1.236, 'grad_norm': 0.004775229669855552, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:12<07:57, 3.64s/it] 75%|███████▌ | 390/520 [24:16<07:53, 3.64s/it] {'loss': 1.2983, 'grad_norm': 0.0039563101119373, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:16<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:19<07:50, 3.65s/it] {'loss': 1.3824, 'grad_norm': 0.004276787687338926, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:19<07:50, 3.65s/it] 75%|███████▌ | 392/520 [24:23<07:47, 3.65s/it] {'loss': 1.1824, 'grad_norm': 0.0038780606561958524, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:23<07:47, 3.65s/it] 76%|███████▌ | 393/520 [24:27<07:43, 3.65s/it] {'loss': 1.249, 'grad_norm': 0.003843905920764169, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:27<07:43, 3.65s/it] 76%|███████▌ | 394/520 [24:30<07:38, 3.64s/it] {'loss': 1.2514, 'grad_norm': 0.004438054783930797, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:30<07:38, 3.64s/it] 76%|███████▌ | 395/520 [24:34<07:34, 3.64s/it] {'loss': 1.2056, 'grad_norm': 0.00414696219270615, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:34<07:34, 3.64s/it] 76%|███████▌ | 396/520 [24:37<07:30, 3.63s/it] {'loss': 1.3015, 'grad_norm': 0.004198495861727321, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:37<07:30, 3.63s/it] 76%|███████▋ | 397/520 [24:41<07:25, 3.62s/it] {'loss': 1.2832, 'grad_norm': 0.0037988107939604943, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:41<07:25, 3.62s/it] 77%|███████▋ | 398/520 [24:45<07:20, 3.61s/it] {'loss': 1.2722, 'grad_norm': 0.004072229810402928, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:45<07:20, 3.61s/it] 77%|███████▋ | 399/520 [24:48<07:16, 3.61s/it] {'loss': 1.3084, 'grad_norm': 0.004099850772340914, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:48<07:16, 3.61s/it] 77%|███████▋ | 400/520 [24:52<07:12, 3.61s/it] {'loss': 1.3743, 'grad_norm': 0.00427965604011675, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:52<07:12, 3.61s/it] 77%|███████▋ | 401/520 [24:55<07:09, 3.61s/it] {'loss': 1.0924, 'grad_norm': 0.003992091355850892, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:55<07:09, 3.61s/it] 77%|███████▋ | 402/520 [24:59<07:05, 3.60s/it] {'loss': 1.2157, 'grad_norm': 0.004138452444748428, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:59<07:05, 3.60s/it] 78%|███████▊ | 403/520 [25:03<07:01, 3.60s/it] {'loss': 1.2509, 'grad_norm': 0.004524497822569178, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:03<07:01, 3.60s/it] 78%|███████▊ | 404/520 [25:06<06:57, 3.60s/it] {'loss': 1.1588, 'grad_norm': 0.005042566691172367, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:06<06:57, 3.60s/it] 78%|███████▊ | 405/520 [25:10<06:53, 3.59s/it] {'loss': 1.3019, 'grad_norm': 0.0038333340361418346, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:10<06:53, 3.59s/it] 78%|███████▊ | 406/520 [25:13<06:51, 3.61s/it] {'loss': 1.2414, 'grad_norm': 0.004675341550863376, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:13<06:51, 3.61s/it] 78%|███████▊ | 407/520 [25:17<06:46, 3.60s/it] {'loss': 1.3508, 'grad_norm': 0.00430925196868412, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:17<06:46, 3.60s/it] 78%|███████▊ | 408/520 [25:21<06:45, 3.62s/it] {'loss': 1.2416, 'grad_norm': 0.004278791144378458, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:21<06:45, 3.62s/it] 79%|███████▊ | 409/520 [25:24<06:41, 3.61s/it] {'loss': 1.3721, 'grad_norm': 0.004565226886170772, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:24<06:41, 3.61s/it] 79%|███████▉ | 410/520 [25:28<06:36, 3.60s/it] {'loss': 1.0756, 'grad_norm': 0.004228122455231795, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:28<06:36, 3.60s/it] 79%|███████▉ | 411/520 [25:31<06:32, 3.60s/it] {'loss': 1.3352, 'grad_norm': 0.004344511022418234, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:31<06:32, 3.60s/it] 79%|███████▉ | 412/520 [25:35<06:30, 3.61s/it] {'loss': 1.2474, 'grad_norm': 0.0040948261335982845, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:35<06:30, 3.61s/it] 79%|███████▉ | 413/520 [25:39<06:27, 3.62s/it] {'loss': 1.342, 'grad_norm': 0.004326636290207469, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:39<06:27, 3.62s/it] 80%|███████▉ | 414/520 [25:42<06:24, 3.63s/it] {'loss': 1.1252, 'grad_norm': 0.0038293374498136103, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:42<06:24, 3.63s/it] 80%|███████▉ | 415/520 [25:46<06:21, 3.64s/it] {'loss': 1.2271, 'grad_norm': 0.003913326125662806, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:46<06:21, 3.64s/it] 80%|████████ | 416/520 [25:50<06:17, 3.63s/it] {'loss': 1.1393, 'grad_norm': 0.004832123481233092, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:50<06:17, 3.63s/it] 80%|████████ | 417/520 [25:53<06:14, 3.63s/it] {'loss': 1.3157, 'grad_norm': 0.004833233999118917, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:53<06:14, 3.63s/it] 80%|████████ | 418/520 [25:57<06:09, 3.62s/it] {'loss': 1.2901, 'grad_norm': 0.003775140606725514, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:57<06:09, 3.62s/it] 81%|████████ | 419/520 [26:00<06:05, 3.62s/it] {'loss': 1.2823, 'grad_norm': 0.004370909430537529, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:00<06:05, 3.62s/it] 81%|████████ | 420/520 [26:04<06:01, 3.61s/it] {'loss': 1.1587, 'grad_norm': 0.004029994467401848, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:04<06:01, 3.61s/it] 81%|████████ | 421/520 [26:08<05:58, 3.62s/it] {'loss': 1.0935, 'grad_norm': 0.004181997369513217, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:08<05:58, 3.62s/it] 81%|████████ | 422/520 [26:11<05:53, 3.61s/it] {'loss': 1.222, 'grad_norm': 0.004329825960734355, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:11<05:53, 3.61s/it] 81%|████████▏ | 423/520 [26:15<05:50, 3.62s/it] {'loss': 1.2164, 'grad_norm': 0.004803369993034784, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:15<05:50, 3.62s/it] 82%|████████▏ | 424/520 [26:19<05:47, 3.62s/it] {'loss': 1.4269, 'grad_norm': 0.004647999219102386, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:19<05:47, 3.62s/it] 82%|████████▏ | 425/520 [26:22<05:43, 3.61s/it] {'loss': 1.2199, 'grad_norm': 0.0038943271206646965, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:22<05:43, 3.61s/it] 82%|████████▏ | 426/520 [26:26<05:38, 3.60s/it] {'loss': 1.237, 'grad_norm': 0.005554709243763775, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:26<05:38, 3.60s/it] 82%|████████▏ | 427/520 [26:29<05:34, 3.60s/it] {'loss': 1.157, 'grad_norm': 0.004426076024269201, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:29<05:34, 3.60s/it] 82%|████████▏ | 428/520 [26:33<05:30, 3.59s/it] {'loss': 1.1239, 'grad_norm': 0.004110303091180217, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:33<05:30, 3.59s/it] 82%|████████▎ | 429/520 [26:37<05:27, 3.60s/it] {'loss': 1.2277, 'grad_norm': 0.003802305823968438, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:37<05:27, 3.60s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:40<05:25, 3.62s/it] {'loss': 1.2256, 'grad_norm': 0.0037077620764046867, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:40<05:25, 3.62s/it] 83%|████████▎ | 431/520 [26:44<05:21, 3.62s/it] {'loss': 1.2969, 'grad_norm': 0.004658504404892528, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:44<05:21, 3.62s/it] 83%|████████▎ | 432/520 [26:47<05:17, 3.61s/it] {'loss': 1.1286, 'grad_norm': 0.004453534470209584, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:47<05:17, 3.61s/it] 83%|████████▎ | 433/520 [26:51<05:13, 3.60s/it] {'loss': 1.2702, 'grad_norm': 0.00411278422557371, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:51<05:13, 3.60s/it] 83%|████████▎ | 434/520 [26:55<05:10, 3.61s/it] {'loss': 1.008, 'grad_norm': 0.0037888525040605304, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:55<05:10, 3.61s/it] 84%|████████▎ | 435/520 [26:58<05:06, 3.60s/it] {'loss': 1.3073, 'grad_norm': 0.0045604757237192916, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:58<05:06, 3.60s/it] 84%|████████▍ | 436/520 [27:02<05:03, 3.61s/it] {'loss': 1.0999, 'grad_norm': 0.004026810861794217, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:02<05:03, 3.61s/it] 84%|████████▍ | 437/520 [27:05<04:59, 3.61s/it] {'loss': 1.3308, 'grad_norm': 0.00395694256588988, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:05<04:59, 3.61s/it] 84%|████████▍ | 438/520 [27:09<04:55, 3.61s/it] {'loss': 1.1319, 'grad_norm': 0.0038722492315842556, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:09<04:55, 3.61s/it] 84%|████████▍ | 439/520 [27:13<04:52, 3.62s/it] {'loss': 1.2639, 'grad_norm': 0.0034959926759151744, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:13<04:52, 3.62s/it] 85%|████████▍ | 440/520 [27:16<04:49, 3.62s/it] {'loss': 1.1877, 'grad_norm': 0.003933918134077107, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:16<04:49, 3.62s/it] 85%|████████▍ | 441/520 [27:20<04:45, 3.62s/it] {'loss': 1.3311, 'grad_norm': 0.00966909500773923, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:20<04:45, 3.62s/it] 85%|████████▌ | 442/520 [27:23<04:41, 3.61s/it] {'loss': 1.2439, 'grad_norm': 0.0047254894112172185, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:23<04:41, 3.61s/it] 85%|████████▌ | 443/520 [27:27<04:37, 3.61s/it] {'loss': 1.2637, 'grad_norm': 0.004369517954831794, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:27<04:37, 3.61s/it] 85%|████████▌ | 444/520 [27:31<04:34, 3.61s/it] {'loss': 1.2283, 'grad_norm': 0.0036104841987051137, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:31<04:34, 3.61s/it] 86%|████████▌ | 445/520 [27:34<04:30, 3.61s/it] {'loss': 1.1472, 'grad_norm': 0.004229853883737942, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:34<04:30, 3.61s/it] 86%|████████▌ | 446/520 [27:38<04:27, 3.61s/it] {'loss': 1.3755, 'grad_norm': 0.003944665199706204, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:38<04:27, 3.61s/it] 86%|████████▌ | 447/520 [27:42<04:24, 3.63s/it] {'loss': 1.2436, 'grad_norm': 0.004005018900200279, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:42<04:24, 3.63s/it] 86%|████████▌ | 448/520 [27:45<04:20, 3.62s/it] {'loss': 1.2136, 'grad_norm': 0.004515116101782461, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:45<04:20, 3.62s/it] 86%|████████▋ | 449/520 [27:49<04:17, 3.62s/it] {'loss': 1.3329, 'grad_norm': 0.0043348127453525604, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:49<04:17, 3.62s/it] 87%|████████▋ | 450/520 [27:52<04:12, 3.61s/it] {'loss': 1.2662, 'grad_norm': 0.004065147932622601, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:52<04:12, 3.61s/it] 87%|████████▋ | 451/520 [27:56<04:13, 3.68s/it] {'loss': 1.2501, 'grad_norm': 0.004111201868593292, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:56<04:13, 3.68s/it] 87%|████████▋ | 452/520 [28:00<04:13, 3.73s/it] {'loss': 1.3724, 'grad_norm': 0.004506456425976872, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:00<04:13, 3.73s/it] 87%|████████▋ | 453/520 [28:04<04:12, 3.77s/it] {'loss': 1.3501, 'grad_norm': 0.004494079470636866, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:04<04:12, 3.77s/it] 87%|████████▋ | 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+[2025-10-17 08:50:47,945] [INFO] [launch.py:348:main] Process 389437 exits successfully. +[2025-10-17 08:50:47,946] [INFO] [launch.py:348:main] Process 389440 exits successfully. +[2025-10-17 08:50:47,946] [INFO] [launch.py:348:main] Process 389443 exits successfully. +[2025-10-17 08:50:48,948] [INFO] [launch.py:348:main] Process 389442 exits successfully. +[2025-10-17 08:50:48,948] [INFO] [launch.py:348:main] Process 389439 exits successfully. +[2025-10-17 08:50:48,949] [INFO] [launch.py:348:main] Process 389441 exits successfully. +[2025-10-17 08:50:48,949] [INFO] [launch.py:348:main] Process 389438 exits successfully. +[2025-10-17 08:50:51,953] [INFO] [launch.py:348:main] Process 389436 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_1.7_2e-1_connector-1.0_1.7_2e-1_ablation_20251017_081656.log +Timestamp: 2025-10-17 08:50:54 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation_20251017_085054.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation_20251017_085054.log new file mode 100644 index 0000000000000000000000000000000000000000..ae5d686ee2266460460876c82fd5d32d4baf5407 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation_20251017_085054.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation_20251017_085054.log +Timestamp: 2025-10-17 08:50:54 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 08:50:57,047] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:50:59,795] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 08:50:59,796] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 1.0 --temperature_attn_text 2.3 --temperature_mlp_text 2.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 1.0 --temperature_attn_vision 2.3 --temperature_mlp_vision 2.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 1.0 --temperature_connector 2.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 08:51:02,357] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:03,438] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 08:51:03,438] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 08:51:03,439] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 08:51:03,439] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 08:51:03,439] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 08:51:03,439] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 08:51:03,439] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 08:51:03,441] [INFO] [launch.py:253:main] process 412335 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,443] [INFO] [launch.py:253:main] process 412336 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,445] [INFO] [launch.py:253:main] process 412337 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,446] [INFO] [launch.py:253:main] process 412338 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,448] [INFO] [launch.py:253:main] process 412339 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,450] [INFO] [launch.py:253:main] process 412340 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,452] [INFO] [launch.py:253:main] process 412341 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 08:51:03,454] [INFO] [launch.py:253:main] process 412342 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 08:51:10,297] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,424] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,464] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,594] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,596] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,610] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,635] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,635] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 08:51:10,705] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:10,828] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:10,872] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,007] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,014] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,020] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,041] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,042] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 08:51:11,042] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.3, 'temperature_mlp': 2.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.3, + "temperature_mlp": 2.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:412335:412335 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:412341:412341 [6] NCCL INFO NET/Plugin: Using internal network plugin. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. 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It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. 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[12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412337:413959 [2] NCCL INFO ncclCommInitRank comm 0x559769b99b40 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412335:413935 [0] NCCL INFO ncclCommInitRank comm 0x55e00727b9e0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412338:413940 [3] NCCL INFO ncclCommInitRank comm 0x557999627840 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:412336:413938 [1] NCCL INFO ncclCommInitRank comm 0x561f08873c00 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412339:413945 [4] NCCL INFO ncclCommInitRank comm 0x562e08a1c330 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412341:413936 [6] NCCL INFO ncclCommInitRank comm 0x55c41abcbf30 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412340:413937 [5] NCCL INFO ncclCommInitRank comm 0x56251f07b480 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xe39e13cad4e8458 - Init COMPLETE +ywang29-vrdb-test1-worker-0:412342:413939 [7] NCCL INFO ncclCommInitRank comm 0x55f605bacf60 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xe39e13cad4e8458 - Init COMPLETE +[2025-10-17 08:51:58,690] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 08:52:00,371] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=1.000000 +Pre-training init connector._connector.0.scores: Mean=1.000005 +Pre-training init connector._connector.2.scores: Mean=0.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 08:52:17,925 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 08:52:17,931 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:003->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412335:418968 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412341:418969 [6] NCCL INFO Connected all rings 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05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412339:418971 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:412337:418975 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xf3ced2aa9c68b76f - Init COMPLETE +ywang29-vrdb-test1-worker-0:412338:418974 [3] NCCL INFO ncclCommInitRank comm 0x7fd83006b490 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xf3ced2aa9c68b76f - Init COMPLETE +ywang29-vrdb-test1-worker-0:412342:418970 [7] NCCL INFO ncclCommInitRank comm 0x7f8bb406aeb0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xf3ced2aa9c68b76f - Init COMPLETE +ywang29-vrdb-test1-worker-0:412340:418972 [5] NCCL INFO ncclCommInitRank comm 0x7fb03806b3a0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xf3ced2aa9c68b76f - Init COMPLETE +ywang29-vrdb-test1-worker-0:412336:418973 [1] NCCL INFO ncclCommInitRank comm 0x7fc93806b050 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xf3ced2aa9c68b76f - Init COMPLETE + 0%| | 1/520 [00:14<2:01:10, 14.01s/it] {'loss': 8.5235, 'grad_norm': 0.39135470954371554, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:01:10, 14.01s/it] 0%| | 2/520 [00:17<1:08:13, 7.90s/it] {'loss': 7.7698, 'grad_norm': 0.40033961788993977, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:08:13, 7.90s/it] 1%| | 3/520 [00:21<51:20, 5.96s/it] {'loss': 7.1513, 'grad_norm': 0.20009993755824826, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<51:20, 5.96s/it] 1%| | 4/520 [00:24<43:28, 5.05s/it] {'loss': 6.3043, 'grad_norm': 0.11811626816804653, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:24<43:28, 5.05s/it] 1%| | 5/520 [00:28<39:04, 4.55s/it] {'loss': 5.2741, 'grad_norm': 0.10193982724163131, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<39:04, 4.55s/it] 1%| | 6/520 [00:32<36:27, 4.26s/it] {'loss': 7.6926, 'grad_norm': 0.4594015829521082, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:27, 4.26s/it] 1%|▏ | 7/520 [00:35<34:36, 4.05s/it] {'loss': 4.8635, 'grad_norm': 0.06104242293848131, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:35<34:36, 4.05s/it] 2%|▏ | 8/520 [00:40<34:59, 4.10s/it] {'loss': 4.6665, 'grad_norm': 0.06982561503938566, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<34:59, 4.10s/it] 2%|▏ | 9/520 [00:44<34:58, 4.11s/it] {'loss': 4.1599, 'grad_norm': 0.048657264205471756, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<34:58, 4.11s/it] 2%|▏ | 10/520 [00:47<33:42, 3.97s/it] {'loss': 3.0313, 'grad_norm': 0.027900928844387687, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:47<33:42, 3.97s/it] 2%|▏ | 11/520 [00:51<33:22, 3.94s/it] {'loss': 3.2826, 'grad_norm': 0.051905999170411735, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:51<33:22, 3.94s/it] 2%|▏ | 12/520 [00:55<32:47, 3.87s/it] {'loss': 3.6371, 'grad_norm': 0.10385900366893912, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<32:47, 3.87s/it][2025-10-17 08:53:22,178] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:59<33:54, 4.01s/it] {'loss': 2.6605, 'grad_norm': 0.02161264412246251, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:59<33:54, 4.01s/it] 3%|▎ | 14/520 [01:03<32:51, 3.90s/it] {'loss': 2.5932, 'grad_norm': 0.017157420040105714, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:03<32:51, 3.90s/it] 3%|▎ | 15/520 [01:07<32:04, 3.81s/it] {'loss': 2.8849, 'grad_norm': 0.04183922224686035, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:07<32:04, 3.81s/it] 3%|▎ | 16/520 [01:10<31:38, 3.77s/it] {'loss': 2.5494, 'grad_norm': 0.018441286029950446, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:10<31:38, 3.77s/it] 3%|▎ | 17/520 [01:14<31:16, 3.73s/it] {'loss': 2.3449, 'grad_norm': 0.018556598984477094, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:14<31:16, 3.73s/it] 3%|▎ | 18/520 [01:18<31:21, 3.75s/it] {'loss': 2.0463, 'grad_norm': 0.019665728080086584, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:18<31:21, 3.75s/it] 4%|▎ | 19/520 [01:22<31:31, 3.78s/it] {'loss': 2.8307, 'grad_norm': 0.08489095974042293, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:22<31:31, 3.78s/it] 4%|▍ | 20/520 [01:25<31:27, 3.77s/it] {'loss': 2.2875, 'grad_norm': 0.04546095215678687, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:25<31:27, 3.77s/it] 4%|▍ | 21/520 [01:29<31:16, 3.76s/it] {'loss': 2.7032, 'grad_norm': 0.037741591174263234, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:29<31:16, 3.76s/it] 4%|▍ | 22/520 [01:33<31:11, 3.76s/it] {'loss': 2.1187, 'grad_norm': 0.01567133578190371, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:33<31:11, 3.76s/it] 4%|▍ | 23/520 [01:37<31:07, 3.76s/it] {'loss': 1.9981, 'grad_norm': 0.012111227868178837, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:37<31:07, 3.76s/it] 5%|▍ | 24/520 [01:40<31:01, 3.75s/it] {'loss': 2.3199, 'grad_norm': 0.017659587853928346, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<31:01, 3.75s/it] 5%|▍ | 25/520 [01:44<30:55, 3.75s/it] {'loss': 2.0452, 'grad_norm': 0.020222948995125663, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:44<30:55, 3.75s/it] 5%|▌ | 26/520 [01:48<30:41, 3.73s/it] {'loss': 1.9767, 'grad_norm': 0.010819899426843842, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<30:41, 3.73s/it] 5%|▌ | 27/520 [01:51<30:26, 3.70s/it] {'loss': 1.8151, 'grad_norm': 0.016860769074019715, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:51<30:26, 3.70s/it] 5%|▌ | 28/520 [01:55<30:31, 3.72s/it] {'loss': 1.7633, 'grad_norm': 0.010766926154834378, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:55<30:31, 3.72s/it] 6%|▌ | 29/520 [01:59<30:30, 3.73s/it] {'loss': 1.7409, 'grad_norm': 0.01005679136907991, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:59<30:30, 3.73s/it] 6%|▌ | 30/520 [02:03<30:29, 3.73s/it] {'loss': 2.2413, 'grad_norm': 0.009274872212699023, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:03<30:29, 3.73s/it] 6%|▌ | 31/520 [02:06<30:19, 3.72s/it] {'loss': 1.7404, 'grad_norm': 0.011917804198775094, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:06<30:19, 3.72s/it] 6%|▌ | 32/520 [02:10<30:17, 3.72s/it] {'loss': 2.3335, 'grad_norm': 0.013530083570140324, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:10<30:17, 3.72s/it] 6%|▋ | 33/520 [02:14<30:05, 3.71s/it] {'loss': 1.7131, 'grad_norm': 0.008344376213960432, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:14<30:05, 3.71s/it] 7%|▋ | 34/520 [02:17<29:45, 3.67s/it] {'loss': 1.681, 'grad_norm': 0.006346593253830383, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:17<29:45, 3.67s/it] 7%|▋ | 35/520 [02:21<29:35, 3.66s/it] {'loss': 1.6675, 'grad_norm': 0.0076008903368543716, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:21<29:35, 3.66s/it] 7%|▋ | 36/520 [02:25<29:25, 3.65s/it] {'loss': 1.7925, 'grad_norm': 0.00623800893236134, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:25<29:25, 3.65s/it] 7%|▋ | 37/520 [02:28<29:21, 3.65s/it] {'loss': 2.0516, 'grad_norm': 0.021696657826696674, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:28<29:21, 3.65s/it] 7%|▋ | 38/520 [02:32<29:20, 3.65s/it] {'loss': 1.8782, 'grad_norm': 0.015214291150241442, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:32<29:20, 3.65s/it] 8%|▊ | 39/520 [02:35<29:12, 3.64s/it] {'loss': 1.6739, 'grad_norm': 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'grad_norm': 0.005810256898772154, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:15<28:23, 3.62s/it] 10%|▉ | 51/520 [03:19<28:21, 3.63s/it] {'loss': 1.5187, 'grad_norm': 0.005305437434279318, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:19<28:21, 3.63s/it] 10%|█ | 52/520 [03:23<28:24, 3.64s/it] {'loss': 1.6726, 'grad_norm': 0.008860270639977531, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:23<28:24, 3.64s/it] 10%|█ | 53/520 [03:26<28:38, 3.68s/it] {'loss': 1.6645, 'grad_norm': 0.005252434257427864, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:26<28:38, 3.68s/it] 10%|█ | 54/520 [03:30<28:59, 3.73s/it] {'loss': 1.5291, 'grad_norm': 0.005912959391551706, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:30<28:59, 3.73s/it] 11%|█ | 55/520 [03:34<29:05, 3.75s/it] {'loss': 1.5297, 'grad_norm': 0.006320634200577182, 'learning_rate': 0.1970596567453391, 'epoch': 0.11} + 11%|█ | 55/520 [03:34<29:05, 3.75s/it] 11%|█ | 56/520 [03:38<29:07, 3.77s/it] {'loss': 1.6759, 'grad_norm': 0.0054246690643924645, 'learning_rate': 0.1969077286229078, 'epoch': 0.11} + 11%|█ | 56/520 [03:38<29:07, 3.77s/it] 11%|█ | 57/520 [03:42<29:10, 3.78s/it] {'loss': 1.5059, 'grad_norm': 0.007310240107849589, 'learning_rate': 0.19675203523431964, 'epoch': 0.11} + 11%|█ | 57/520 [03:42<29:10, 3.78s/it] 11%|█ | 58/520 [03:46<29:15, 3.80s/it] {'loss': 1.6837, 'grad_norm': 0.005787522037825068, 'learning_rate': 0.19659258262890683, 'epoch': 0.11} + 11%|█ | 58/520 [03:46<29:15, 3.80s/it] 11%|█▏ | 59/520 [03:49<29:16, 3.81s/it] {'loss': 1.66, 'grad_norm': 0.006077949402765502, 'learning_rate': 0.19642937700206278, 'epoch': 0.11} + 11%|█▏ | 59/520 [03:49<29:16, 3.81s/it] 12%|█▏ | 60/520 [03:53<29:17, 3.82s/it] {'loss': 1.5971, 'grad_norm': 0.008404646974010941, 'learning_rate': 0.19626242469500121, 'epoch': 0.12} + 12%|█▏ | 60/520 [03:53<29:17, 3.82s/it] 12%|█▏ | 61/520 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{'loss': 1.3739, 'grad_norm': 0.004485919966435778, 'learning_rate': 0.09314872070816434, 'epoch': 0.54} + 54%|█████▎ | 279/520 [17:24<14:39, 3.65s/it] 54%|█████▍ | 280/520 [17:28<14:39, 3.66s/it] {'loss': 1.2962, 'grad_norm': 0.004215256949958898, 'learning_rate': 0.09252699064135758, 'epoch': 0.54} + 54%|█████▍ | 280/520 [17:28<14:39, 3.66s/it] 54%|█████▍ | 281/520 [17:32<14:33, 3.66s/it] {'loss': 1.4012, 'grad_norm': 0.0038625535701206208, 'learning_rate': 0.09190555093187967, 'epoch': 0.54} + 54%|█████▍ | 281/520 [17:32<14:33, 3.66s/it] 54%|█████▍ | 282/520 [17:35<14:29, 3.66s/it] {'loss': 1.2402, 'grad_norm': 0.00343599856219453, 'learning_rate': 0.09128442572523418, 'epoch': 0.54} + 54%|█████▍ | 282/520 [17:35<14:29, 3.66s/it] 54%|█████▍ | 283/520 [17:39<14:25, 3.65s/it] {'loss': 1.4241, 'grad_norm': 0.004313511295564648, 'learning_rate': 0.09066363915470495, 'epoch': 0.54} + 54%|█████▍ | 283/520 [17:39<14:25, 3.65s/it] 55%|█████▍ | 284/520 [17:43<14:23, 3.66s/it] {'loss': 1.3457, 'grad_norm': 0.00442162131379324, 'learning_rate': 0.09004321534041836, 'epoch': 0.55} + 55%|█████▍ | 284/520 [17:43<14:23, 3.66s/it] 55%|█████▍ | 285/520 [17:46<14:19, 3.66s/it] {'loss': 1.2744, 'grad_norm': 0.003915459812845989, 'learning_rate': 0.08942317838840624, 'epoch': 0.55} + 55%|█████▍ | 285/520 [17:46<14:19, 3.66s/it] 55%|█████▌ | 286/520 [17:50<14:14, 3.65s/it] {'loss': 1.1394, 'grad_norm': 0.004143308101542936, 'learning_rate': 0.08880355238966922, 'epoch': 0.55} + 55%|█████▌ | 286/520 [17:50<14:14, 3.65s/it] 55%|█████▌ | 287/520 [17:54<14:12, 3.66s/it] {'loss': 1.3976, 'grad_norm': 0.0040216024965500025, 'learning_rate': 0.08818436141924073, 'epoch': 0.55} + 55%|█████▌ | 287/520 [17:54<14:12, 3.66s/it] 55%|█████▌ | 288/520 [17:57<14:04, 3.64s/it] {'loss': 1.4441, 'grad_norm': 0.003944983102980846, 'learning_rate': 0.08756562953525152, 'epoch': 0.55} + 55%|█████▌ | 288/520 [17:57<14:04, 3.64s/it] 56%|█████▌ | 289/520 [18:01<14:00, 3.64s/it] {'loss': 1.2929, 'grad_norm': 0.0034626844283658286, 'learning_rate': 0.08694738077799487, 'epoch': 0.56} + 56%|█████▌ | 289/520 [18:01<14:00, 3.64s/it] 56%|█████▌ | 290/520 [18:04<13:52, 3.62s/it] {'loss': 1.2039, 'grad_norm': 0.003406015789983572, 'learning_rate': 0.08632963916899268, 'epoch': 0.56} + 56%|█████▌ | 290/520 [18:04<13:52, 3.62s/it] 56%|█████▌ | 291/520 [18:08<13:49, 3.62s/it] {'loss': 1.2671, 'grad_norm': 0.004071918905574612, 'learning_rate': 0.08571242871006202, 'epoch': 0.56} + 56%|█████▌ | 291/520 [18:08<13:49, 3.62s/it] 56%|█████▌ | 292/520 [18:12<13:43, 3.61s/it] {'loss': 1.3105, 'grad_norm': 0.003437016252262741, 'learning_rate': 0.08509577338238256, 'epoch': 0.56} + 56%|█████▌ | 292/520 [18:12<13:43, 3.61s/it] 56%|█████▋ | 293/520 [18:15<13:38, 3.61s/it] {'loss': 1.2525, 'grad_norm': 0.003804630756934808, 'learning_rate': 0.08447969714556484, 'epoch': 0.56} + 56%|█████▋ | 293/520 [18:15<13:38, 3.61s/it] 57%|█████▋ | 294/520 [18:19<13:35, 3.61s/it] {'loss': 1.2856, 'grad_norm': 0.003892025412083441, 'learning_rate': 0.08386422393671933, 'epoch': 0.57} + 57%|█████▋ | 294/520 [18:19<13:35, 3.61s/it] 57%|█████▋ | 295/520 [18:23<13:31, 3.61s/it] {'loss': 1.4218, 'grad_norm': 0.005074363684964378, 'learning_rate': 0.08324937766952638, 'epoch': 0.57} + 57%|█████▋ | 295/520 [18:23<13:31, 3.61s/it] 57%|█████▋ | 296/520 [18:26<13:27, 3.60s/it] {'loss': 1.2263, 'grad_norm': 0.003867763144246398, 'learning_rate': 0.08263518223330697, 'epoch': 0.57} + 57%|█████▋ | 296/520 [18:26<13:27, 3.60s/it] 57%|█████▋ | 297/520 [18:30<13:22, 3.60s/it] {'loss': 1.3721, 'grad_norm': 0.00394638125369802, 'learning_rate': 0.08202166149209474, 'epoch': 0.57} + 57%|█████▋ | 297/520 [18:30<13:22, 3.60s/it] 57%|█████▋ | 298/520 [18:33<13:17, 3.59s/it] {'loss': 1.3276, 'grad_norm': 0.0033766338956703816, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:33<13:17, 3.59s/it] 57%|█████▊ | 299/520 [18:37<13:13, 3.59s/it] {'loss': 1.428, 'grad_norm': 0.0036326118333781065, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:37<13:13, 3.59s/it] 58%|█████▊ | 300/520 [18:40<13:10, 3.59s/it] {'loss': 1.3849, 'grad_norm': 0.0037026776415979303, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:40<13:10, 3.59s/it] 58%|█████▊ | 301/520 [18:44<13:06, 3.59s/it] {'loss': 1.3554, 'grad_norm': 0.0036568805923053805, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:44<13:06, 3.59s/it] 58%|█████▊ | 302/520 [18:48<13:02, 3.59s/it] {'loss': 1.4431, 'grad_norm': 0.0044598685366664045, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:48<13:02, 3.59s/it] 58%|█████▊ | 303/520 [18:51<12:59, 3.59s/it] {'loss': 1.2933, 'grad_norm': 0.003899025162159858, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:51<12:59, 3.59s/it] 58%|█████▊ | 304/520 [18:55<12:58, 3.60s/it] {'loss': 1.3336, 'grad_norm': 0.003952269200120353, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [18:55<12:58, 3.60s/it] 59%|█████▊ | 305/520 [18:58<12:53, 3.60s/it] {'loss': 1.4022, 'grad_norm': 0.004113044536001592, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [18:58<12:53, 3.60s/it] 59%|█████▉ | 306/520 [19:02<12:49, 3.60s/it] {'loss': 1.3277, 'grad_norm': 0.003577017242965219, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:02<12:49, 3.60s/it] 59%|█████▉ | 307/520 [19:06<13:06, 3.69s/it] {'loss': 1.2712, 'grad_norm': 0.0035834793797846474, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:06<13:06, 3.69s/it] 59%|█████▉ | 308/520 [19:10<12:55, 3.66s/it] {'loss': 1.3953, 'grad_norm': 0.0034827070213127647, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:10<12:55, 3.66s/it] 59%|█████▉ | 309/520 [19:13<12:47, 3.64s/it] {'loss': 1.2676, 'grad_norm': 0.003374322671931844, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:13<12:47, 3.64s/it] 60%|█████▉ | 310/520 [19:17<12:40, 3.62s/it] {'loss': 1.2473, 'grad_norm': 0.003646674712249218, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:17<12:40, 3.62s/it] 60%|█████▉ | 311/520 [19:20<12:35, 3.62s/it] {'loss': 1.2192, 'grad_norm': 0.0035497204864493237, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:20<12:35, 3.62s/it] 60%|██████ | 312/520 [19:24<12:33, 3.62s/it] {'loss': 1.2049, 'grad_norm': 0.004069462202304509, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:24<12:33, 3.62s/it] 60%|██████ | 313/520 [19:28<12:30, 3.62s/it] {'loss': 1.2034, 'grad_norm': 0.003318409494062338, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:28<12:30, 3.62s/it] 60%|██████ | 314/520 [19:32<12:46, 3.72s/it] {'loss': 1.2352, 'grad_norm': 0.0034322806303290865, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:32<12:46, 3.72s/it] 61%|██████ | 315/520 [19:35<12:35, 3.68s/it] {'loss': 1.4042, 'grad_norm': 0.004519815605605601, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:35<12:35, 3.68s/it] 61%|██████ | 316/520 [19:39<12:54, 3.80s/it] {'loss': 1.2065, 'grad_norm': 0.004197854152714484, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:39<12:54, 3.80s/it] 61%|██████ | 317/520 [19:43<12:39, 3.74s/it] {'loss': 1.2321, 'grad_norm': 0.003266474378814634, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:43<12:39, 3.74s/it] 61%|██████ | 318/520 [19:46<12:28, 3.70s/it] {'loss': 1.3589, 'grad_norm': 0.003865675432633035, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:46<12:28, 3.70s/it] 61%|██████▏ | 319/520 [19:50<12:38, 3.78s/it] {'loss': 1.2195, 'grad_norm': 0.003648481209361014, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:50<12:38, 3.78s/it] 62%|██████▏ | 320/520 [19:54<12:26, 3.73s/it] {'loss': 1.1601, 'grad_norm': 0.0038770425729092407, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:54<12:26, 3.73s/it] 62%|██████▏ | 321/520 [19:58<12:17, 3.71s/it] {'loss': 1.3726, 'grad_norm': 0.003975331764495512, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [19:58<12:17, 3.71s/it] 62%|██████▏ | 322/520 [20:01<12:09, 3.68s/it] {'loss': 1.2579, 'grad_norm': 0.0037814571144303586, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:01<12:09, 3.68s/it] 62%|██████▏ | 323/520 [20:05<12:04, 3.68s/it] {'loss': 1.341, 'grad_norm': 0.004359813756884978, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:05<12:04, 3.68s/it] 62%|██████▏ | 324/520 [20:09<11:58, 3.67s/it] {'loss': 1.295, 'grad_norm': 0.0045645157130493755, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:09<11:58, 3.67s/it] 62%|██████▎ | 325/520 [20:12<11:53, 3.66s/it] {'loss': 1.3135, 'grad_norm': 0.003918126878459061, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:12<11:53, 3.66s/it] 63%|██████▎ | 326/520 [20:16<11:50, 3.66s/it] {'loss': 1.2911, 'grad_norm': 0.0037040800008645586, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:16<11:50, 3.66s/it] 63%|██████▎ | 327/520 [20:20<11:45, 3.65s/it] {'loss': 1.4126, 'grad_norm': 0.0045778203265612, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:20<11:45, 3.65s/it] 63%|██████▎ | 328/520 [20:23<11:39, 3.64s/it] {'loss': 1.3611, 'grad_norm': 0.004171294032466974, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:23<11:39, 3.64s/it] 63%|██████▎ | 329/520 [20:27<11:33, 3.63s/it] {'loss': 1.2061, 'grad_norm': 0.003182202721742736, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:27<11:33, 3.63s/it] 63%|██████▎ | 330/520 [20:30<11:32, 3.64s/it] {'loss': 1.2815, 'grad_norm': 0.003286197838419992, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:30<11:32, 3.64s/it] 64%|██████▎ | 331/520 [20:34<11:38, 3.69s/it] {'loss': 1.25, 'grad_norm': 0.003491949017820312, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:34<11:38, 3.69s/it] 64%|██████▍ | 332/520 [20:38<11:42, 3.74s/it] {'loss': 1.4123, 'grad_norm': 0.003961179821701042, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:38<11:42, 3.74s/it] 64%|██████▍ | 333/520 [20:42<11:42, 3.76s/it] {'loss': 1.4145, 'grad_norm': 0.004018491490934039, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:42<11:42, 3.76s/it] 64%|██████▍ | 334/520 [20:46<11:40, 3.77s/it] {'loss': 1.2977, 'grad_norm': 0.004252044885407218, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:46<11:40, 3.77s/it] 64%|██████▍ | 335/520 [20:49<11:31, 3.74s/it] {'loss': 1.289, 'grad_norm': 0.00325579789007877, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:49<11:31, 3.74s/it] 65%|██████▍ | 336/520 [20:53<11:22, 3.71s/it] {'loss': 1.1755, 'grad_norm': 0.004047461168898875, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:53<11:22, 3.71s/it] 65%|██████▍ | 337/520 [20:57<11:14, 3.69s/it] {'loss': 1.1795, 'grad_norm': 0.0037558393555422653, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:57<11:14, 3.69s/it] 65%|██████▌ | 338/520 [21:00<11:09, 3.68s/it] {'loss': 1.3042, 'grad_norm': 0.003620014344023806, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:00<11:09, 3.68s/it] 65%|██████▌ | 339/520 [21:04<11:04, 3.67s/it] {'loss': 1.2434, 'grad_norm': 0.003564589802053869, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:04<11:04, 3.67s/it] 65%|██████▌ | 340/520 [21:08<11:00, 3.67s/it] {'loss': 1.2355, 'grad_norm': 0.003630694027969668, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:08<11:00, 3.67s/it] 66%|██████▌ | 341/520 [21:11<10:58, 3.68s/it] {'loss': 1.2546, 'grad_norm': 0.003879005458416913, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:11<10:58, 3.68s/it] 66%|██████▌ | 342/520 [21:15<10:51, 3.66s/it] {'loss': 1.3886, 'grad_norm': 0.004749054399683636, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:15<10:51, 3.66s/it] 66%|██████▌ | 343/520 [21:19<10:49, 3.67s/it] {'loss': 1.3486, 'grad_norm': 0.004095795123847614, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:19<10:49, 3.67s/it] 66%|██████▌ | 344/520 [21:22<10:44, 3.66s/it] {'loss': 1.203, 'grad_norm': 0.004007401726655807, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:22<10:44, 3.66s/it] 66%|██████▋ | 345/520 [21:26<10:38, 3.65s/it] {'loss': 1.3267, 'grad_norm': 0.004329072934536201, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:26<10:38, 3.65s/it] 67%|██████▋ | 346/520 [21:29<10:33, 3.64s/it] {'loss': 1.3364, 'grad_norm': 0.0038894257856872307, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:29<10:33, 3.64s/it] 67%|██████▋ | 347/520 [21:33<10:29, 3.64s/it] {'loss': 1.2275, 'grad_norm': 0.00348983721037557, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:33<10:29, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:37<10:25, 3.63s/it] {'loss': 1.1814, 'grad_norm': 0.004450564543363741, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:37<10:25, 3.63s/it] 67%|██████▋ | 349/520 [21:40<10:22, 3.64s/it] {'loss': 1.2309, 'grad_norm': 0.004058996277773803, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:40<10:22, 3.64s/it] 67%|██████▋ | 350/520 [21:44<10:17, 3.63s/it] {'loss': 1.262, 'grad_norm': 0.003907738383479426, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:44<10:17, 3.63s/it] 68%|██████▊ | 351/520 [21:48<10:15, 3.64s/it] {'loss': 1.1628, 'grad_norm': 0.0033577898935800783, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:48<10:15, 3.64s/it] 68%|██████▊ | 352/520 [21:51<10:12, 3.64s/it] {'loss': 1.2946, 'grad_norm': 0.0035955435112588574, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:51<10:12, 3.64s/it] 68%|██████▊ | 353/520 [21:55<10:10, 3.66s/it] {'loss': 1.2812, 'grad_norm': 0.003133337771336547, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:55<10:10, 3.66s/it] 68%|██████▊ | 354/520 [21:59<10:05, 3.65s/it] {'loss': 1.4222, 'grad_norm': 0.003772903641271153, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:59<10:05, 3.65s/it] 68%|██████▊ | 355/520 [22:02<09:59, 3.63s/it] {'loss': 1.2332, 'grad_norm': 0.003690554427986993, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:02<09:59, 3.63s/it] 68%|██████▊ | 356/520 [22:06<09:55, 3.63s/it] {'loss': 1.2335, 'grad_norm': 0.003783044944938042, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:06<09:55, 3.63s/it] 69%|██████▊ | 357/520 [22:10<09:53, 3.64s/it] {'loss': 1.2477, 'grad_norm': 0.00322362104564831, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:10<09:53, 3.64s/it] 69%|██████▉ | 358/520 [22:13<09:51, 3.65s/it] {'loss': 1.1825, 'grad_norm': 0.0037127497286017688, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:13<09:51, 3.65s/it] 69%|██████▉ | 359/520 [22:17<09:47, 3.65s/it] {'loss': 1.3536, 'grad_norm': 0.004246831734802005, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:17<09:47, 3.65s/it] 69%|██████▉ | 360/520 [22:21<09:46, 3.67s/it] {'loss': 1.3676, 'grad_norm': 0.004329835040577228, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:21<09:46, 3.67s/it] 69%|██████▉ | 361/520 [22:24<09:54, 3.74s/it] {'loss': 1.3535, 'grad_norm': 0.003574433667834694, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:24<09:54, 3.74s/it] 70%|██████▉ | 362/520 [22:29<10:10, 3.86s/it] {'loss': 1.2479, 'grad_norm': 0.0038336901083940588, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:29<10:10, 3.86s/it] 70%|██████▉ | 363/520 [22:32<10:05, 3.85s/it] {'loss': 1.2743, 'grad_norm': 0.0034966596078806777, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:32<10:05, 3.85s/it] 70%|███████ | 364/520 [22:36<10:00, 3.85s/it] {'loss': 1.3721, 'grad_norm': 0.0035755670646154805, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:36<10:00, 3.85s/it] 70%|███████ | 365/520 [22:40<09:55, 3.84s/it] {'loss': 1.3375, 'grad_norm': 0.0037899478173288523, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:40<09:55, 3.84s/it] 70%|███████ | 366/520 [22:44<09:51, 3.84s/it] {'loss': 1.2882, 'grad_norm': 0.0032568260922682902, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:44<09:51, 3.84s/it] 71%|███████ | 367/520 [22:48<09:46, 3.84s/it] {'loss': 1.2876, 'grad_norm': 0.003532334436798711, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:48<09:46, 3.84s/it] 71%|███████ | 368/520 [22:52<09:44, 3.84s/it] {'loss': 1.1357, 'grad_norm': 0.004250514562646025, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:52<09:44, 3.84s/it] 71%|███████ | 369/520 [22:55<09:41, 3.85s/it] {'loss': 1.3264, 'grad_norm': 0.0034836867423431108, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:55<09:41, 3.85s/it] 71%|███████ | 370/520 [22:59<09:36, 3.84s/it] {'loss': 1.196, 'grad_norm': 0.003193728626562863, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:59<09:36, 3.84s/it] 71%|███████▏ | 371/520 [23:03<09:32, 3.84s/it] {'loss': 1.196, 'grad_norm': 0.003650322270001297, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:03<09:32, 3.84s/it] 72%|███████▏ | 372/520 [23:07<09:29, 3.85s/it] {'loss': 1.4217, 'grad_norm': 0.003469423681501364, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:07<09:29, 3.85s/it] 72%|███████▏ | 373/520 [23:11<09:25, 3.85s/it] {'loss': 1.2951, 'grad_norm': 0.0038995961739984315, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:11<09:25, 3.85s/it] 72%|███████▏ | 374/520 [23:15<09:21, 3.84s/it] {'loss': 1.2786, 'grad_norm': 0.0035479079933222364, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:15<09:21, 3.84s/it] 72%|███████▏ | 375/520 [23:19<09:17, 3.85s/it] {'loss': 1.1841, 'grad_norm': 0.0036420612751858266, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:19<09:17, 3.85s/it] 72%|███████▏ | 376/520 [23:22<09:08, 3.81s/it] {'loss': 1.3162, 'grad_norm': 0.0035003379414884303, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:22<09:08, 3.81s/it] 72%|███████▎ | 377/520 [23:26<08:56, 3.75s/it] {'loss': 1.2526, 'grad_norm': 0.004093282239235837, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:26<08:56, 3.75s/it] 73%|███████▎ | 378/520 [23:30<08:47, 3.71s/it] {'loss': 1.2956, 'grad_norm': 0.0035218341207839867, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:30<08:47, 3.71s/it] 73%|███████▎ | 379/520 [23:33<08:39, 3.69s/it] {'loss': 1.2858, 'grad_norm': 0.0033165986301451553, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:33<08:39, 3.69s/it] 73%|███████▎ | 380/520 [23:37<08:32, 3.66s/it] {'loss': 1.4073, 'grad_norm': 0.0045307792524292515, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:37<08:32, 3.66s/it] 73%|███████▎ | 381/520 [23:40<08:27, 3.65s/it] {'loss': 1.2761, 'grad_norm': 0.003521304556723681, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:40<08:27, 3.65s/it] 73%|███████▎ | 382/520 [23:44<08:24, 3.66s/it] {'loss': 1.34, 'grad_norm': 0.003470199699451184, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:44<08:24, 3.66s/it] 74%|███████▎ | 383/520 [23:48<08:19, 3.64s/it] {'loss': 1.1175, 'grad_norm': 0.003914781770773064, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:48<08:19, 3.64s/it] 74%|███████▍ | 384/520 [23:51<08:16, 3.65s/it] {'loss': 1.4548, 'grad_norm': 0.0037553381351364103, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:51<08:16, 3.65s/it] 74%|███████▍ | 385/520 [23:55<08:11, 3.64s/it] {'loss': 1.2596, 'grad_norm': 0.0034301593182051275, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:55<08:11, 3.64s/it] 74%|███████▍ | 386/520 [23:59<08:07, 3.64s/it] {'loss': 1.2038, 'grad_norm': 0.0030511838366304284, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:59<08:07, 3.64s/it] 74%|███████▍ | 387/520 [24:02<08:04, 3.64s/it] {'loss': 1.424, 'grad_norm': 0.003794444158813119, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:02<08:04, 3.64s/it] 75%|███████▍ | 388/520 [24:06<08:01, 3.65s/it] {'loss': 1.1545, 'grad_norm': 0.003270774595450123, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:06<08:01, 3.65s/it] 75%|███████▍ | 389/520 [24:10<07:58, 3.66s/it] {'loss': 1.2212, 'grad_norm': 0.0038506840040264433, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:10<07:58, 3.66s/it] 75%|███████▌ | 390/520 [24:13<07:56, 3.67s/it] {'loss': 1.2736, 'grad_norm': 0.003423531138343102, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:13<07:56, 3.67s/it] 75%|███████▌ | 391/520 [24:17<07:53, 3.67s/it] {'loss': 1.3561, 'grad_norm': 0.003527073480848294, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:17<07:53, 3.67s/it] 75%|███████▌ | 392/520 [24:21<07:51, 3.68s/it] {'loss': 1.1635, 'grad_norm': 0.003357239266632505, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:21<07:51, 3.68s/it] 76%|███████▌ | 393/520 [24:24<07:46, 3.68s/it] {'loss': 1.2226, 'grad_norm': 0.0031382644717891507, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:24<07:46, 3.68s/it] 76%|███████▌ | 394/520 [24:28<07:40, 3.66s/it] {'loss': 1.2335, 'grad_norm': 0.0038842232381015107, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:28<07:40, 3.66s/it] 76%|███████▌ | 395/520 [24:32<07:37, 3.66s/it] {'loss': 1.1898, 'grad_norm': 0.0038311618284447724, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:32<07:37, 3.66s/it] 76%|███████▌ | 396/520 [24:35<07:33, 3.66s/it] {'loss': 1.2809, 'grad_norm': 0.0036584964032666473, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:35<07:33, 3.66s/it] 76%|███████▋ | 397/520 [24:39<07:32, 3.68s/it] {'loss': 1.2632, 'grad_norm': 0.003319252047424888, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:39<07:32, 3.68s/it] 77%|███████▋ | 398/520 [24:43<07:33, 3.72s/it] {'loss': 1.253, 'grad_norm': 0.0036411846712970926, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:43<07:33, 3.72s/it] 77%|███████▋ | 399/520 [24:47<07:33, 3.75s/it] {'loss': 1.2768, 'grad_norm': 0.0036154057365170604, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:47<07:33, 3.75s/it] 77%|███████▋ | 400/520 [24:50<07:32, 3.77s/it] {'loss': 1.3236, 'grad_norm': 0.003560065590894073, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:50<07:32, 3.77s/it] 77%|███████▋ | 401/520 [24:54<07:30, 3.78s/it] {'loss': 1.0833, 'grad_norm': 0.003756163022050913, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:54<07:30, 3.78s/it] 77%|███████▋ | 402/520 [24:58<07:26, 3.78s/it] {'loss': 1.1908, 'grad_norm': 0.0034826437315186158, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:58<07:26, 3.78s/it] 78%|███████▊ | 403/520 [25:02<07:23, 3.79s/it] {'loss': 1.2345, 'grad_norm': 0.0038305850624973064, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:02<07:23, 3.79s/it] 78%|███████▊ | 404/520 [25:06<07:19, 3.79s/it] {'loss': 1.141, 'grad_norm': 0.004439057277295419, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:06<07:19, 3.79s/it] 78%|███████▊ | 405/520 [25:09<07:16, 3.80s/it] {'loss': 1.2751, 'grad_norm': 0.0034870995673090158, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:09<07:16, 3.80s/it] 78%|███████▊ | 406/520 [25:13<07:12, 3.79s/it] {'loss': 1.2171, 'grad_norm': 0.004207762187081535, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:13<07:12, 3.79s/it] 78%|███████▊ | 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79%|███████▉ | 412/520 [25:36<06:53, 3.83s/it] {'loss': 1.2358, 'grad_norm': 0.003469558217181315, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:36<06:53, 3.83s/it] 79%|███████▉ | 413/520 [25:40<06:43, 3.77s/it] {'loss': 1.3128, 'grad_norm': 0.0034907360012452634, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:40<06:43, 3.77s/it] 80%|███████▉ | 414/520 [25:43<06:36, 3.74s/it] {'loss': 1.0995, 'grad_norm': 0.0030503764044874015, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:43<06:36, 3.74s/it] 80%|███████▉ | 415/520 [25:47<06:29, 3.71s/it] {'loss': 1.2098, 'grad_norm': 0.0034156174030708203, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:47<06:29, 3.71s/it] 80%|████████ | 416/520 [25:51<06:23, 3.69s/it] {'loss': 1.1244, 'grad_norm': 0.004078556234046615, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:51<06:23, 3.69s/it] 80%|████████ | 417/520 [25:54<06:19, 3.68s/it] {'loss': 1.3002, 'grad_norm': 0.003970691049633962, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:54<06:19, 3.68s/it] 80%|████████ | 418/520 [25:58<06:14, 3.67s/it] {'loss': 1.2764, 'grad_norm': 0.0033691237984174627, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:58<06:14, 3.67s/it] 81%|████████ | 419/520 [26:02<06:09, 3.66s/it] {'loss': 1.2587, 'grad_norm': 0.003921790051339545, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:02<06:09, 3.66s/it] 81%|████████ | 420/520 [26:05<06:05, 3.66s/it] {'loss': 1.1401, 'grad_norm': 0.003665175912979948, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:05<06:05, 3.66s/it] 81%|████████ | 421/520 [26:09<06:03, 3.67s/it] {'loss': 1.0759, 'grad_norm': 0.0036111059968629783, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:09<06:03, 3.67s/it] 81%|████████ | 422/520 [26:13<05:59, 3.67s/it] {'loss': 1.2039, 'grad_norm': 0.004200018994974191, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:13<05:59, 3.67s/it] 81%|████████▏ | 423/520 [26:16<05:55, 3.66s/it] {'loss': 1.198, 'grad_norm': 0.004108282993784782, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:16<05:55, 3.66s/it] 82%|████████▏ | 424/520 [26:20<05:51, 3.66s/it] {'loss': 1.39, 'grad_norm': 0.0041163982563187295, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:20<05:51, 3.66s/it] 82%|████████▏ | 425/520 [26:24<05:47, 3.65s/it] {'loss': 1.1977, 'grad_norm': 0.0033421297793850544, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:24<05:47, 3.65s/it] 82%|████████▏ | 426/520 [26:27<05:43, 3.65s/it] {'loss': 1.2293, 'grad_norm': 0.0047570189827926285, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:27<05:43, 3.65s/it] 82%|████████▏ | 427/520 [26:31<05:38, 3.64s/it] {'loss': 1.1343, 'grad_norm': 0.0035298297268954813, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:31<05:38, 3.64s/it] 82%|████████▏ | 428/520 [26:35<05:34, 3.64s/it] {'loss': 1.1063, 'grad_norm': 0.0035230260272556824, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:35<05:34, 3.64s/it] 82%|████████▎ | 429/520 [26:38<05:31, 3.64s/it] {'loss': 1.2172, 'grad_norm': 0.0034156130687512565, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:38<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:42<05:28, 3.65s/it] {'loss': 1.2042, 'grad_norm': 0.0031794976829662243, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:42<05:28, 3.65s/it] 83%|████████▎ | 431/520 [26:46<05:24, 3.65s/it] {'loss': 1.2684, 'grad_norm': 0.003986666060483522, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:46<05:24, 3.65s/it] 83%|████████▎ | 432/520 [26:49<05:21, 3.65s/it] {'loss': 1.1139, 'grad_norm': 0.003883959511710326, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:49<05:21, 3.65s/it] 83%|████████▎ | 433/520 [26:53<05:16, 3.64s/it] {'loss': 1.2569, 'grad_norm': 0.0035282434565686175, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:53<05:16, 3.64s/it] 83%|████████▎ | 434/520 [26:56<05:14, 3.65s/it] {'loss': 0.9965, 'grad_norm': 0.003460904613025027, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:56<05:14, 3.65s/it] 84%|████████▎ | 435/520 [27:00<05:09, 3.64s/it] {'loss': 1.2884, 'grad_norm': 0.0041950218722731, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:00<05:09, 3.64s/it] 84%|████████▍ | 436/520 [27:04<05:05, 3.64s/it] {'loss': 1.0811, 'grad_norm': 0.0034138050890129227, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:04<05:05, 3.64s/it] 84%|████████▍ | 437/520 [27:07<05:01, 3.63s/it] {'loss': 1.317, 'grad_norm': 0.003510781950412414, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:07<05:01, 3.63s/it] 84%|████████▍ | 438/520 [27:11<04:59, 3.65s/it] {'loss': 1.1184, 'grad_norm': 0.003504194003221874, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:11<04:59, 3.65s/it] 84%|████████▍ | 439/520 [27:15<04:56, 3.66s/it] {'loss': 1.2422, 'grad_norm': 0.0036570760391396785, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:15<04:56, 3.66s/it] 85%|████████▍ | 440/520 [27:18<04:52, 3.65s/it] {'loss': 1.1734, 'grad_norm': 0.0036298077381627936, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:18<04:52, 3.65s/it] 85%|████████▍ | 441/520 [27:22<04:48, 3.65s/it] {'loss': 1.2688, 'grad_norm': 0.003427738039683916, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:22<04:48, 3.65s/it] 85%|████████▌ | 442/520 [27:26<04:44, 3.65s/it] {'loss': 1.2219, 'grad_norm': 0.004035363684542472, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:26<04:44, 3.65s/it] 85%|████████▌ | 443/520 [27:29<04:41, 3.65s/it] {'loss': 1.2482, 'grad_norm': 0.0036497185731645863, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:29<04:41, 3.65s/it] 85%|████████▌ | 444/520 [27:33<04:38, 3.67s/it] {'loss': 1.2124, 'grad_norm': 0.0032078940141462057, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:33<04:38, 3.67s/it] 86%|████████▌ | 445/520 [27:37<04:34, 3.66s/it] {'loss': 1.1332, 'grad_norm': 0.0037970210525749156, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:37<04:34, 3.66s/it] 86%|████████▌ | 446/520 [27:40<04:32, 3.69s/it] {'loss': 1.347, 'grad_norm': 0.003631064068066613, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:40<04:32, 3.69s/it] 86%|████████▌ | 447/520 [27:44<04:29, 3.69s/it] {'loss': 1.2208, 'grad_norm': 0.0035187214113329775, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:44<04:29, 3.69s/it] 86%|████████▌ | 448/520 [27:48<04:25, 3.69s/it] {'loss': 1.1975, 'grad_norm': 0.0037696600541564907, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:48<04:25, 3.69s/it] 86%|████████▋ | 449/520 [27:51<04:21, 3.68s/it] {'loss': 1.3012, 'grad_norm': 0.0038683075228141558, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:51<04:21, 3.68s/it] 87%|████████▋ | 450/520 [27:55<04:17, 3.68s/it] {'loss': 1.2395, 'grad_norm': 0.003524874636975072, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:55<04:17, 3.68s/it] 87%|████████▋ | 451/520 [27:59<04:13, 3.68s/it] {'loss': 1.2331, 'grad_norm': 0.0035359024345832505, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:59<04:13, 3.68s/it] 87%|████████▋ | 452/520 [28:02<04:09, 3.67s/it] {'loss': 1.3385, 'grad_norm': 0.0034982654113694927, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:02<04:09, 3.67s/it] 87%|████████▋ | 453/520 [28:06<04:05, 3.66s/it] {'loss': 1.3142, 'grad_norm': 0.00409030519497401, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:06<04:05, 3.66s/it] 87%|████████▋ | 454/520 [28:10<04:02, 3.67s/it] {'loss': 1.1473, 'grad_norm': 0.003987596593164173, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:10<04:02, 3.67s/it] 88%|████████▊ | 455/520 [28:13<03:57, 3.66s/it] {'loss': 1.2781, 'grad_norm': 0.003500946909265335, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:13<03:57, 3.66s/it] 88%|████████▊ | 456/520 [28:17<03:56, 3.70s/it] {'loss': 1.1993, 'grad_norm': 0.003532610444754912, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:17<03:56, 3.70s/it] 88%|████████▊ | 457/520 [28:21<03:55, 3.73s/it] {'loss': 1.2843, 'grad_norm': 0.0032370576830697893, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:21<03:55, 3.73s/it] 88%|████████▊ | 458/520 [28:25<03:52, 3.76s/it] {'loss': 1.3413, 'grad_norm': 0.0036384162319098878, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:25<03:52, 3.76s/it] 88%|████████▊ | 459/520 [28:29<03:50, 3.78s/it] {'loss': 1.2729, 'grad_norm': 0.003613724445135647, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:29<03:50, 3.78s/it] 88%|████████▊ | 460/520 [28:32<03:47, 3.79s/it] {'loss': 1.1419, 'grad_norm': 0.003345630142979864, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:33<03:47, 3.79s/it] 89%|████████▊ | 461/520 [28:36<03:43, 3.79s/it] {'loss': 1.3631, 'grad_norm': 0.002938815382097615, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:36<03:43, 3.79s/it] 89%|████████▉ | 462/520 [28:40<03:40, 3.79s/it] {'loss': 1.3936, 'grad_norm': 0.0035575626293444214, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:40<03:40, 3.79s/it] 89%|████████▉ | 463/520 [28:44<03:36, 3.80s/it] {'loss': 1.1077, 'grad_norm': 0.00371955264900499, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:44<03:36, 3.80s/it] 89%|████████▉ | 464/520 [28:48<03:33, 3.81s/it] {'loss': 1.2538, 'grad_norm': 0.003969204728630893, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:48<03:33, 3.81s/it] 89%|████████▉ | 465/520 [28:52<03:29, 3.81s/it] {'loss': 1.3737, 'grad_norm': 0.004209766463495701, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:52<03:29, 3.81s/it] 90%|████████▉ | 466/520 [28:55<03:25, 3.81s/it] {'loss': 1.2384, 'grad_norm': 0.0031720047591087632, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:55<03:25, 3.81s/it] 90%|████████▉ | 467/520 [28:59<03:22, 3.83s/it] {'loss': 1.2688, 'grad_norm': 0.0035474650563144723, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:59<03:22, 3.83s/it] 90%|█████████ | 468/520 [29:03<03:18, 3.82s/it] {'loss': 1.2168, 'grad_norm': 0.0039817084422281886, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:03<03:18, 3.82s/it] 90%|█████████ | 469/520 [29:07<03:14, 3.81s/it] {'loss': 1.2736, 'grad_norm': 0.003696546865726876, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:07<03:14, 3.81s/it] 90%|█████████ | 470/520 [29:11<03:10, 3.81s/it] {'loss': 1.1531, 'grad_norm': 0.00310681148113461, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:11<03:10, 3.81s/it] 91%|█████████ | 471/520 [29:14<03:06, 3.80s/it] {'loss': 1.1657, 'grad_norm': 0.003808577545178428, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:14<03:06, 3.80s/it] 91%|█████████ | 472/520 [29:18<03:02, 3.80s/it] {'loss': 1.1405, 'grad_norm': 0.0035338411317336824, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:18<03:02, 3.80s/it] 91%|█████████ | 473/520 [29:22<02:58, 3.81s/it] {'loss': 1.2013, 'grad_norm': 0.003523657496870884, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:22<02:58, 3.81s/it] 91%|█████████ | 474/520 [29:26<02:54, 3.80s/it] {'loss': 1.304, 'grad_norm': 0.003559754796752954, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:26<02:54, 3.80s/it] 91%|█████████▏| 475/520 [29:30<02:50, 3.80s/it] {'loss': 1.2181, 'grad_norm': 0.003325501476995395, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:30<02:50, 3.80s/it] 92%|█████████▏| 476/520 [29:33<02:46, 3.79s/it] {'loss': 1.1954, 'grad_norm': 0.0035885172855855347, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:33<02:46, 3.79s/it] 92%|█████████▏| 477/520 [29:37<02:43, 3.80s/it] {'loss': 1.1842, 'grad_norm': 0.004089913587604459, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:37<02:43, 3.80s/it] 92%|█████████▏| 478/520 [29:41<02:40, 3.81s/it] {'loss': 1.1438, 'grad_norm': 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100%|██████████| 520/520 [32:18<00:00, 3.73s/it] +[2025-10-17 09:24:46,603] [INFO] [launch.py:348:main] Process 412341 exits successfully. +[2025-10-17 09:24:46,603] [INFO] [launch.py:348:main] Process 412339 exits successfully. +[2025-10-17 09:24:46,604] [INFO] [launch.py:348:main] Process 412336 exits successfully. +[2025-10-17 09:24:47,605] [INFO] [launch.py:348:main] Process 412337 exits successfully. +[2025-10-17 09:24:47,606] [INFO] [launch.py:348:main] Process 412338 exits successfully. +[2025-10-17 09:24:47,606] [INFO] [launch.py:348:main] Process 412342 exits successfully. +[2025-10-17 09:24:47,606] [INFO] [launch.py:348:main] Process 412340 exits successfully. +[2025-10-17 09:24:51,611] [INFO] [launch.py:348:main] Process 412335 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.3_2e-1_connector-1.0_2.3_2e-1_ablation_20251017_085054.log +Timestamp: 2025-10-17 09:24:54 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation_20251017_092454.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation_20251017_092454.log new file mode 100644 index 0000000000000000000000000000000000000000..45ae737f7ed3d28e0c9fbd97f635b040f52d880b --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation_20251017_092454.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation_20251017_092454.log +Timestamp: 2025-10-17 09:24:54 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 09:24:56,731] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:24:59,647] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 09:24:59,648] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 1.0 --temperature_attn_text 2.9 --temperature_mlp_text 2.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 1.0 --temperature_attn_vision 2.9 --temperature_mlp_vision 2.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 1.0 --temperature_connector 2.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 09:25:02,200] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:03,277] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 09:25:03,277] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 09:25:03,277] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 09:25:03,277] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 09:25:03,277] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 09:25:03,277] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 09:25:03,277] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 09:25:03,279] [INFO] [launch.py:253:main] process 434836 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,281] [INFO] [launch.py:253:main] process 434837 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,283] [INFO] [launch.py:253:main] process 434838 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,285] [INFO] [launch.py:253:main] process 434839 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,287] [INFO] [launch.py:253:main] process 434840 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,289] [INFO] [launch.py:253:main] process 434841 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,291] [INFO] [launch.py:253:main] process 434842 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 09:25:03,292] [INFO] [launch.py:253:main] process 434843 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '1.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '1.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '1.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 09:25:09,963] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,259] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,260] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,260] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,332] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,334] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,335] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,337] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 09:25:10,380] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,661] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,661] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 09:25:10,662] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,679] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,753] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,753] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,754] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 09:25:10,757] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.9, 'temperature_mlp': 2.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.9, + "temperature_mlp": 2.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:434836:434836 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Using network Socket +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:434842:434842 [6] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:434837:434837 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:434838:436411 [2] 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4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434840:436394 [4] NCCL INFO ncclCommInitRank comm 0x55d6d29d6980 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434841:436414 [5] NCCL INFO ncclCommInitRank comm 0x5584e3dd8b10 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434843:436412 [7] NCCL INFO ncclCommInitRank comm 0x56433fd096f0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434839:436413 [3] NCCL INFO ncclCommInitRank comm 0x55bb6cbbd490 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434836:436392 [0] NCCL INFO ncclCommInitRank comm 0x55fb1d7a2280 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434837:436415 [1] NCCL INFO ncclCommInitRank comm 0x555fae2a6040 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:434842:436393 [6] NCCL INFO ncclCommInitRank comm 0x5569c9b42160 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x13fe2ece15dde88d - Init COMPLETE +ywang29-vrdb-test1-worker-0:434838:436411 [2] NCCL INFO ncclCommInitRank comm 0x55b0e001cb20 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x13fe2ece15dde88d - Init COMPLETE +[2025-10-17 09:25:57,735] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 09:25:59,383] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=1.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=1.000000 +Pre-training init connector._connector.0.scores: Mean=1.000005 +Pre-training init connector._connector.2.scores: Mean=0.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 09:41:41,800 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 09:41:41,809 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:003->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:434840:441716 [4] NCCL INFO ncclCommInitRank comm 0x7fe91c06b5a0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434836:441715 [0] NCCL INFO ncclCommInitRank comm 0x7fb49006ae80 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434838:441722 [2] NCCL INFO ncclCommInitRank comm 0x7fc5e806a4b0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434842:441717 [6] NCCL INFO ncclCommInitRank comm 0x7f9b2406b310 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434839:441721 [3] NCCL INFO ncclCommInitRank comm 0x7f32c006af70 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434843:441720 [7] NCCL INFO ncclCommInitRank comm 0x7fcaec06a8e0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434841:441719 [5] NCCL INFO ncclCommInitRank comm 0x7fcdc006b490 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x57a72ebaf89edb3a - Init COMPLETE +ywang29-vrdb-test1-worker-0:434837:441718 [1] NCCL INFO ncclCommInitRank comm 0x7f354806b0b0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x57a72ebaf89edb3a - Init COMPLETE + 0%| | 1/520 [00:14<2:05:09, 14.47s/it] {'loss': 9.1604, 'grad_norm': 0.40180197840667375, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:05:09, 14.47s/it] 0%| | 2/520 [00:18<1:10:33, 8.17s/it] {'loss': 8.2213, 'grad_norm': 0.3836491420336032, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:10:33, 8.17s/it] 1%| | 3/520 [00:21<52:30, 6.09s/it] {'loss': 7.014, 'grad_norm': 0.09217969205478815, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<52:30, 6.09s/it] 1%| | 4/520 [00:25<44:01, 5.12s/it] {'loss': 6.5896, 'grad_norm': 0.09800915312760433, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<44:01, 5.12s/it] 1%| | 5/520 [00:29<39:28, 4.60s/it] {'loss': 6.0354, 'grad_norm': 0.07773725583424411, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<39:28, 4.60s/it] 1%| | 6/520 [00:32<36:42, 4.29s/it] {'loss': 7.0371, 'grad_norm': 0.24366247086899545, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:42, 4.29s/it] 1%|▏ | 7/520 [00:36<35:02, 4.10s/it] {'loss': 5.5533, 'grad_norm': 0.0957647775144254, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<35:02, 4.10s/it] 2%|▏ | 8/520 [00:40<35:23, 4.15s/it] {'loss': 5.4021, 'grad_norm': 0.0862099515379537, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<35:23, 4.15s/it] 2%|▏ | 9/520 [00:44<35:18, 4.15s/it] {'loss': 4.7137, 'grad_norm': 0.050079401492718716, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<35:18, 4.15s/it] 2%|▏ | 10/520 [00:48<33:54, 3.99s/it] {'loss': 3.4897, 'grad_norm': 0.04009360430127729, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<33:54, 3.99s/it] 2%|▏ | 11/520 [00:52<33:18, 3.93s/it] {'loss': 4.0098, 'grad_norm': 0.08942478006769111, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:52<33:18, 3.93s/it] 2%|▏ | 12/520 [00:55<32:29, 3.84s/it] {'loss': 4.8342, 'grad_norm': 0.09940821174772661, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:56<32:29, 3.84s/it][2025-10-17 09:42:47,040] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:00<33:38, 3.98s/it] {'loss': 3.4838, 'grad_norm': 0.027916119477073856, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:00<33:38, 3.98s/it] 3%|▎ | 14/520 [01:03<32:41, 3.88s/it] {'loss': 3.3864, 'grad_norm': 0.022683778270897938, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:03<32:41, 3.88s/it] 3%|▎ | 15/520 [01:07<32:03, 3.81s/it] {'loss': 3.7396, 'grad_norm': 0.02878381880264677, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:07<32:03, 3.81s/it] 3%|▎ | 16/520 [01:11<31:30, 3.75s/it] {'loss': 3.4296, 'grad_norm': 0.12980886411299986, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:11<31:30, 3.75s/it] 3%|▎ | 17/520 [01:14<31:09, 3.72s/it] {'loss': 2.8314, 'grad_norm': 0.013059822353091003, 'learning_rate': 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23/520 [01:36<30:16, 3.65s/it] {'loss': 2.1197, 'grad_norm': 0.015616409616401063, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:36<30:16, 3.65s/it] 5%|▍ | 24/520 [01:40<30:04, 3.64s/it] {'loss': 2.2887, 'grad_norm': 0.01029327023598796, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<30:04, 3.64s/it] 5%|▍ | 25/520 [01:43<30:01, 3.64s/it] {'loss': 2.0667, 'grad_norm': 0.0119840319125151, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:43<30:01, 3.64s/it] 5%|▌ | 26/520 [01:47<30:01, 3.65s/it] {'loss': 1.9759, 'grad_norm': 0.008209410428622737, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:47<30:01, 3.65s/it] 5%|▌ | 27/520 [01:51<29:55, 3.64s/it] {'loss': 1.794, 'grad_norm': 0.008044044291248865, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:51<29:55, 3.64s/it] 5%|▌ | 28/520 [01:54<29:50, 3.64s/it] {'loss': 1.7399, 'grad_norm': 0.006819418789722988, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:54<29:50, 3.64s/it] 6%|▌ | 29/520 [01:58<29:46, 3.64s/it] {'loss': 1.753, 'grad_norm': 0.007060143124348662, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:58<29:46, 3.64s/it] 6%|▌ | 30/520 [02:02<29:47, 3.65s/it] {'loss': 2.1462, 'grad_norm': 0.009201327291286318, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:02<29:47, 3.65s/it] 6%|▌ | 31/520 [02:05<29:42, 3.64s/it] {'loss': 1.7313, 'grad_norm': 0.006785597156121533, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:05<29:42, 3.64s/it] 6%|▌ | 32/520 [02:09<29:38, 3.64s/it] {'loss': 2.3109, 'grad_norm': 0.01658541226014015, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:09<29:38, 3.64s/it] 6%|▋ | 33/520 [02:13<29:35, 3.65s/it] {'loss': 1.6944, 'grad_norm': 0.00955806709987089, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:13<29:35, 3.65s/it] 7%|▋ | 34/520 [02:16<29:28, 3.64s/it] {'loss': 1.6634, 'grad_norm': 0.006918686106914905, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:16<29:28, 3.64s/it] 7%|▋ | 35/520 [02:20<29:24, 3.64s/it] {'loss': 1.6609, 'grad_norm': 0.005398880863531845, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:20<29:24, 3.64s/it] 7%|▋ | 36/520 [02:23<29:21, 3.64s/it] {'loss': 1.7815, 'grad_norm': 0.00568056720990653, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:23<29:21, 3.64s/it] 7%|▋ | 37/520 [02:27<29:18, 3.64s/it] {'loss': 2.0737, 'grad_norm': 0.018188352308770677, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:27<29:18, 3.64s/it] 7%|▋ | 38/520 [02:31<29:13, 3.64s/it] {'loss': 1.8482, 'grad_norm': 0.007208650002055535, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:31<29:13, 3.64s/it] 8%|▊ | 39/520 [02:34<29:07, 3.63s/it] {'loss': 1.67, 'grad_norm': 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'epoch': 0.11} + 11%|█ | 55/520 [03:35<29:07, 3.76s/it] 11%|█ | 56/520 [03:38<29:24, 3.80s/it] {'loss': 1.6605, 'grad_norm': 0.005375251879832023, 'learning_rate': 0.1969077286229078, 'epoch': 0.11} + 11%|█ | 56/520 [03:38<29:24, 3.80s/it] 11%|█ | 57/520 [03:42<29:31, 3.83s/it] {'loss': 1.5182, 'grad_norm': 0.006339738269801047, 'learning_rate': 0.19675203523431964, 'epoch': 0.11} + 11%|█ | 57/520 [03:42<29:31, 3.83s/it] 11%|█ | 58/520 [03:46<29:36, 3.85s/it] {'loss': 1.6685, 'grad_norm': 0.003951367546254607, 'learning_rate': 0.19659258262890683, 'epoch': 0.11} + 11%|█ | 58/520 [03:46<29:36, 3.85s/it] 11%|█▏ | 59/520 [03:50<29:35, 3.85s/it] {'loss': 1.6362, 'grad_norm': 0.008022072374369232, 'learning_rate': 0.19642937700206278, 'epoch': 0.11} + 11%|█▏ | 59/520 [03:50<29:35, 3.85s/it] 12%|█▏ | 60/520 [03:54<29:37, 3.86s/it] {'loss': 1.5865, 'grad_norm': 0.006448642794385051, 'learning_rate': 0.19626242469500121, 'epoch': 0.12} + 12%|█▏ | 60/520 [03:54<29:37, 3.86s/it] 12%|█▏ | 61/520 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0.0031448264749715372, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:07<11:39, 3.86s/it] 65%|██████▌ | 340/520 [21:10<11:35, 3.86s/it] {'loss': 1.2279, 'grad_norm': 0.0031801498834646354, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:10<11:35, 3.86s/it] 66%|██████▌ | 341/520 [21:14<11:31, 3.86s/it] {'loss': 1.2498, 'grad_norm': 0.003498969676991312, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:14<11:31, 3.86s/it] 66%|██████▌ | 342/520 [21:18<11:26, 3.86s/it] {'loss': 1.3705, 'grad_norm': 0.003485757109064326, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:18<11:26, 3.86s/it] 66%|██████▌ | 343/520 [21:22<11:25, 3.87s/it] {'loss': 1.34, 'grad_norm': 0.0031341685058339816, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:22<11:25, 3.87s/it] 66%|██████▌ | 344/520 [21:26<11:21, 3.87s/it] {'loss': 1.1966, 'grad_norm': 0.0032649110275787254, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:26<11:21, 3.87s/it] 66%|██████▋ | 345/520 [21:30<11:17, 3.87s/it] {'loss': 1.3183, 'grad_norm': 0.0037221500639727065, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:30<11:17, 3.87s/it] 67%|██████▋ | 346/520 [21:34<11:13, 3.87s/it] {'loss': 1.3299, 'grad_norm': 0.003035141859014425, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:34<11:13, 3.87s/it] 67%|██████▋ | 347/520 [21:37<11:09, 3.87s/it] {'loss': 1.2139, 'grad_norm': 0.0030519370330787865, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:37<11:09, 3.87s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:41<11:05, 3.87s/it] {'loss': 1.1725, 'grad_norm': 0.0038361297934345734, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:41<11:05, 3.87s/it] 67%|██████▋ | 349/520 [21:45<11:01, 3.87s/it] {'loss': 1.221, 'grad_norm': 0.0032710321361543028, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:45<11:01, 3.87s/it] 67%|██████▋ | 350/520 [21:49<10:57, 3.87s/it] {'loss': 1.2566, 'grad_norm': 0.0032431189150309877, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:49<10:57, 3.87s/it] 68%|██████▊ | 351/520 [21:53<10:54, 3.87s/it] {'loss': 1.1594, 'grad_norm': 0.002903989708629848, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:53<10:54, 3.87s/it] 68%|██████▊ | 352/520 [21:57<10:51, 3.88s/it] {'loss': 1.2879, 'grad_norm': 0.003196619288820971, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:57<10:51, 3.88s/it] 68%|██████▊ | 353/520 [22:01<10:51, 3.90s/it] {'loss': 1.272, 'grad_norm': 0.0027668859686857503, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:01<10:51, 3.90s/it] 68%|██████▊ | 354/520 [22:05<10:46, 3.89s/it] {'loss': 1.413, 'grad_norm': 0.0031133033518838844, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:05<10:46, 3.89s/it] 68%|██████▊ | 355/520 [22:09<10:41, 3.89s/it] {'loss': 1.2285, 'grad_norm': 0.003222868064986881, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:09<10:41, 3.89s/it] 68%|██████▊ | 356/520 [22:12<10:36, 3.88s/it] {'loss': 1.2293, 'grad_norm': 0.0032338886701412705, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:12<10:36, 3.88s/it] 69%|██████▊ | 357/520 [22:16<10:32, 3.88s/it] {'loss': 1.247, 'grad_norm': 0.0028249791000215384, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:16<10:32, 3.88s/it] 69%|██████▉ | 358/520 [22:20<10:28, 3.88s/it] {'loss': 1.1765, 'grad_norm': 0.0030495534023119125, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:20<10:28, 3.88s/it] 69%|██████▉ | 359/520 [22:24<10:15, 3.82s/it] {'loss': 1.3461, 'grad_norm': 0.00341574677532644, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:24<10:15, 3.82s/it] 69%|██████▉ | 360/520 [22:28<10:02, 3.76s/it] {'loss': 1.3568, 'grad_norm': 0.0034573142685032613, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:28<10:02, 3.76s/it] 69%|██████▉ | 361/520 [22:31<09:52, 3.73s/it] {'loss': 1.3443, 'grad_norm': 0.0028875312747636884, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:31<09:52, 3.73s/it] 70%|██████▉ | 362/520 [22:35<09:43, 3.69s/it] {'loss': 1.2364, 'grad_norm': 0.003345238135418267, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:35<09:43, 3.69s/it] 70%|██████▉ | 363/520 [22:38<09:37, 3.68s/it] {'loss': 1.2711, 'grad_norm': 0.0030619578239900946, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:38<09:37, 3.68s/it] 70%|███████ | 364/520 [22:42<09:32, 3.67s/it] {'loss': 1.3624, 'grad_norm': 0.0029967897727769996, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:42<09:32, 3.67s/it] 70%|███████ | 365/520 [22:46<09:26, 3.66s/it] {'loss': 1.3302, 'grad_norm': 0.0032404104723835476, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:46<09:26, 3.66s/it] 70%|███████ | 366/520 [22:49<09:22, 3.65s/it] {'loss': 1.2849, 'grad_norm': 0.0028122729543746954, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:49<09:22, 3.65s/it] 71%|███████ | 367/520 [22:53<09:23, 3.68s/it] {'loss': 1.2815, 'grad_norm': 0.003048994367245465, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:53<09:23, 3.68s/it] 71%|███████ | 368/520 [22:57<09:26, 3.73s/it] {'loss': 1.1309, 'grad_norm': 0.0033952154788622264, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:57<09:26, 3.73s/it] 71%|███████ | 369/520 [23:01<09:27, 3.76s/it] {'loss': 1.3168, 'grad_norm': 0.0028743688376924826, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:01<09:27, 3.76s/it] 71%|███████ | 370/520 [23:05<09:26, 3.77s/it] {'loss': 1.1929, 'grad_norm': 0.0031551805011831692, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:05<09:26, 3.77s/it] 71%|███████▏ | 371/520 [23:08<09:20, 3.76s/it] {'loss': 1.1866, 'grad_norm': 0.0030089243367306152, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:08<09:20, 3.76s/it] 72%|███████▏ | 372/520 [23:12<09:10, 3.72s/it] {'loss': 1.4169, 'grad_norm': 0.0029086342992538363, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:12<09:10, 3.72s/it] 72%|███████▏ | 373/520 [23:16<09:03, 3.70s/it] {'loss': 1.2889, 'grad_norm': 0.0031963771481762875, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:16<09:03, 3.70s/it] 72%|███████▏ | 374/520 [23:19<08:57, 3.68s/it] {'loss': 1.2746, 'grad_norm': 0.003068267867997039, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:19<08:57, 3.68s/it] 72%|███████▏ | 375/520 [23:23<08:51, 3.66s/it] {'loss': 1.1858, 'grad_norm': 0.0031617009924357086, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:23<08:51, 3.66s/it] 72%|███████▏ | 376/520 [23:26<08:45, 3.65s/it] {'loss': 1.3019, 'grad_norm': 0.002822569113964383, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:26<08:45, 3.65s/it] 72%|███████▎ | 377/520 [23:30<08:41, 3.64s/it] {'loss': 1.2392, 'grad_norm': 0.003394979589874131, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:30<08:41, 3.64s/it] 73%|███████▎ | 378/520 [23:34<08:36, 3.63s/it] {'loss': 1.2918, 'grad_norm': 0.0030795314131920573, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:34<08:36, 3.63s/it] 73%|███████▎ | 379/520 [23:37<08:31, 3.63s/it] {'loss': 1.2805, 'grad_norm': 0.002953993752752945, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:37<08:31, 3.63s/it] 73%|███████▎ | 380/520 [23:41<08:27, 3.62s/it] {'loss': 1.3894, 'grad_norm': 0.003420248998315062, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:41<08:27, 3.62s/it] 73%|███████▎ | 381/520 [23:45<08:29, 3.66s/it] {'loss': 1.268, 'grad_norm': 0.0030353799055352184, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:45<08:29, 3.66s/it] 73%|███████▎ | 382/520 [23:48<08:30, 3.70s/it] {'loss': 1.3313, 'grad_norm': 0.0031313216986761064, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:48<08:30, 3.70s/it] 74%|███████▎ | 383/520 [23:52<08:30, 3.73s/it] {'loss': 1.1076, 'grad_norm': 0.0031646838007161355, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:52<08:30, 3.73s/it] 74%|███████▍ | 384/520 [23:56<08:29, 3.74s/it] {'loss': 1.4431, 'grad_norm': 0.0031512015179296408, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:56<08:29, 3.74s/it] 74%|███████▍ | 385/520 [24:00<08:22, 3.72s/it] {'loss': 1.2514, 'grad_norm': 0.0029957088939660606, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:00<08:22, 3.72s/it] 74%|███████▍ | 386/520 [24:03<08:14, 3.69s/it] {'loss': 1.2002, 'grad_norm': 0.0027356298276997055, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:03<08:14, 3.69s/it] 74%|███████▍ | 387/520 [24:07<08:09, 3.68s/it] {'loss': 1.415, 'grad_norm': 0.0030471323883230392, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:07<08:09, 3.68s/it] 75%|███████▍ | 388/520 [24:11<08:03, 3.66s/it] {'loss': 1.1493, 'grad_norm': 0.0028441787587898384, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:11<08:03, 3.66s/it] 75%|███████▍ | 389/520 [24:14<07:57, 3.65s/it] {'loss': 1.2126, 'grad_norm': 0.0036054946424693007, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:14<07:57, 3.65s/it] 75%|███████▌ | 390/520 [24:18<07:53, 3.64s/it] {'loss': 1.2701, 'grad_norm': 0.0028850158785673798, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:18<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:21<07:49, 3.64s/it] {'loss': 1.354, 'grad_norm': 0.0030647573951849954, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:21<07:49, 3.64s/it] 75%|███████▌ | 392/520 [24:25<07:49, 3.67s/it] {'loss': 1.1627, 'grad_norm': 0.002887306790436517, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:25<07:49, 3.67s/it] 76%|███████▌ | 393/520 [24:29<07:53, 3.73s/it] {'loss': 1.2157, 'grad_norm': 0.0028391403455473438, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:29<07:53, 3.73s/it] 76%|███████▌ | 394/520 [24:33<07:52, 3.75s/it] {'loss': 1.2291, 'grad_norm': 0.003384028388472424, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:33<07:52, 3.75s/it] 76%|███████▌ | 395/520 [24:37<07:51, 3.77s/it] {'loss': 1.1843, 'grad_norm': 0.0033446318789110926, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:37<07:51, 3.77s/it] 76%|███████▌ | 396/520 [24:40<07:41, 3.72s/it] {'loss': 1.2701, 'grad_norm': 0.003102209757421408, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:40<07:41, 3.72s/it] 76%|███████▋ | 397/520 [24:44<07:34, 3.69s/it] {'loss': 1.2589, 'grad_norm': 0.00291600504180683, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:44<07:34, 3.69s/it] 77%|███████▋ | 398/520 [24:48<07:28, 3.67s/it] {'loss': 1.2505, 'grad_norm': 0.0031402271331204355, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:48<07:28, 3.67s/it] 77%|███████▋ | 399/520 [24:51<07:23, 3.67s/it] {'loss': 1.2666, 'grad_norm': 0.0030334406008593554, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:51<07:23, 3.67s/it] 77%|███████▋ | 400/520 [24:55<07:19, 3.66s/it] {'loss': 1.315, 'grad_norm': 0.0029886965706893524, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:55<07:19, 3.66s/it] 77%|███████▋ | 401/520 [24:58<07:14, 3.65s/it] {'loss': 1.0792, 'grad_norm': 0.003165984337134298, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:58<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:02<07:10, 3.65s/it] {'loss': 1.1948, 'grad_norm': 0.0031946516220689746, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:02<07:10, 3.65s/it] 78%|███████▊ | 403/520 [25:06<07:05, 3.64s/it] {'loss': 1.2269, 'grad_norm': 0.003372000049483864, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:06<07:05, 3.64s/it] 78%|███████▊ | 404/520 [25:09<07:01, 3.63s/it] {'loss': 1.1381, 'grad_norm': 0.0037678984236479483, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:09<07:01, 3.63s/it] 78%|███████▊ | 405/520 [25:13<06:57, 3.63s/it] {'loss': 1.2648, 'grad_norm': 0.0028767810885195907, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:13<06:57, 3.63s/it] 78%|███████▊ | 406/520 [25:17<06:55, 3.64s/it] {'loss': 1.2116, 'grad_norm': 0.0036293703466272675, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:17<06:55, 3.64s/it] 78%|███████▊ | 407/520 [25:20<06:51, 3.64s/it] {'loss': 1.3259, 'grad_norm': 0.0030800802332914285, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:20<06:51, 3.64s/it] 78%|███████▊ | 408/520 [25:24<06:45, 3.62s/it] {'loss': 1.2167, 'grad_norm': 0.003278916469156, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:24<06:45, 3.62s/it] 79%|███████▊ | 409/520 [25:27<06:41, 3.62s/it] {'loss': 1.3453, 'grad_norm': 0.0036312360478735544, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:27<06:41, 3.62s/it] 79%|███████▉ | 410/520 [25:31<06:36, 3.61s/it] {'loss': 1.0614, 'grad_norm': 0.0030156587388965256, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:31<06:36, 3.61s/it] 79%|███████▉ | 411/520 [25:35<06:33, 3.61s/it] {'loss': 1.31, 'grad_norm': 0.0034029566423396758, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:35<06:33, 3.61s/it] 79%|███████▉ | 412/520 [25:38<06:31, 3.62s/it] {'loss': 1.2294, 'grad_norm': 0.0030069850028793177, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:38<06:31, 3.62s/it] 79%|███████▉ | 413/520 [25:42<06:28, 3.63s/it] {'loss': 1.3054, 'grad_norm': 0.0030296375878329086, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:42<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:46<06:25, 3.63s/it] {'loss': 1.0965, 'grad_norm': 0.002726764949846373, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:46<06:25, 3.63s/it] 80%|███████▉ | 415/520 [25:49<06:21, 3.64s/it] {'loss': 1.2047, 'grad_norm': 0.0029430695185608644, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:49<06:21, 3.64s/it] 80%|████████ | 416/520 [25:53<06:18, 3.64s/it] {'loss': 1.1194, 'grad_norm': 0.003311174642693772, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:53<06:18, 3.64s/it] 80%|████████ | 417/520 [25:57<06:14, 3.64s/it] {'loss': 1.2938, 'grad_norm': 0.003544633077360021, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:57<06:14, 3.64s/it] 80%|████████ | 418/520 [26:00<06:10, 3.63s/it] {'loss': 1.2721, 'grad_norm': 0.002935343345747121, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:00<06:10, 3.63s/it] 81%|████████ | 419/520 [26:04<06:07, 3.63s/it] {'loss': 1.2562, 'grad_norm': 0.003240213934487804, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:04<06:07, 3.63s/it] 81%|████████ | 420/520 [26:07<06:03, 3.64s/it] {'loss': 1.1394, 'grad_norm': 0.0031813270320420273, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:07<06:03, 3.64s/it] 81%|████████ | 421/520 [26:11<06:00, 3.64s/it] {'loss': 1.07, 'grad_norm': 0.003790879981023745, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:11<06:00, 3.64s/it] 81%|████████ | 422/520 [26:15<05:56, 3.64s/it] {'loss': 1.2013, 'grad_norm': 0.003373983851003915, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:15<05:56, 3.64s/it] 81%|████████▏ | 423/520 [26:18<05:52, 3.64s/it] {'loss': 1.1877, 'grad_norm': 0.0035723691781712704, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:18<05:52, 3.64s/it] 82%|████████▏ | 424/520 [26:22<05:50, 3.65s/it] {'loss': 1.3843, 'grad_norm': 0.0034107508407563948, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:22<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:26<05:46, 3.64s/it] {'loss': 1.1916, 'grad_norm': 0.0029872933001295326, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:26<05:46, 3.64s/it] 82%|████████▏ | 426/520 [26:29<05:42, 3.64s/it] {'loss': 1.2238, 'grad_norm': 0.004151724953596535, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:29<05:42, 3.64s/it] 82%|████████▏ | 427/520 [26:33<05:37, 3.63s/it] {'loss': 1.1339, 'grad_norm': 0.0030129719471504927, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:33<05:37, 3.63s/it] 82%|████████▏ | 428/520 [26:37<05:34, 3.63s/it] {'loss': 1.0982, 'grad_norm': 0.003087908515672329, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:37<05:34, 3.63s/it] 82%|████████▎ | 429/520 [26:40<05:30, 3.63s/it] {'loss': 1.2097, 'grad_norm': 0.0030068853192721477, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:40<05:30, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:44<05:26, 3.63s/it] {'loss': 1.2083, 'grad_norm': 0.002879242200775893, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:44<05:26, 3.63s/it] 83%|████████▎ | 431/520 [26:47<05:24, 3.65s/it] {'loss': 1.2646, 'grad_norm': 0.003275405387244461, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:47<05:24, 3.65s/it] 83%|████████▎ | 432/520 [26:51<05:25, 3.70s/it] {'loss': 1.1137, 'grad_norm': 0.003357560363432753, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:51<05:25, 3.70s/it] 83%|████████▎ | 433/520 [26:55<05:25, 3.74s/it] {'loss': 1.2519, 'grad_norm': 0.002998708548657299, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:55<05:25, 3.74s/it] 83%|████████▎ | 434/520 [26:59<05:23, 3.77s/it] {'loss': 0.9898, 'grad_norm': 0.0028825553629553543, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:59<05:23, 3.77s/it] 84%|████████▎ | 435/520 [27:03<05:21, 3.79s/it] {'loss': 1.2854, 'grad_norm': 0.0035116492348627043, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:03<05:21, 3.79s/it] 84%|████████▍ | 436/520 [27:07<05:18, 3.79s/it] {'loss': 1.0777, 'grad_norm': 0.00307020111189686, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:07<05:18, 3.79s/it] 84%|████████▍ | 437/520 [27:10<05:15, 3.80s/it] {'loss': 1.3142, 'grad_norm': 0.0030502953376749437, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:10<05:15, 3.80s/it] 84%|████████▍ | 438/520 [27:14<05:12, 3.81s/it] {'loss': 1.119, 'grad_norm': 0.003019326001949415, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:14<05:12, 3.81s/it] 84%|████████▍ | 439/520 [27:18<05:10, 3.84s/it] {'loss': 1.2332, 'grad_norm': 0.002885075398598546, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:18<05:10, 3.84s/it] 85%|████████▍ | 440/520 [27:22<05:06, 3.83s/it] {'loss': 1.1643, 'grad_norm': 0.0030296681976478036, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:22<05:06, 3.83s/it] 85%|████████▍ | 441/520 [27:26<05:01, 3.82s/it] {'loss': 1.2679, 'grad_norm': 0.003099363495152022, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:26<05:01, 3.82s/it] 85%|████████▌ | 442/520 [27:29<04:54, 3.78s/it] {'loss': 1.2182, 'grad_norm': 0.0034527951839920137, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:29<04:54, 3.78s/it] 85%|████████▌ | 443/520 [27:33<04:47, 3.74s/it] {'loss': 1.2396, 'grad_norm': 0.003209405257412056, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:33<04:47, 3.74s/it] 85%|████████▌ | 444/520 [27:37<04:42, 3.72s/it] {'loss': 1.2099, 'grad_norm': 0.0027906519131700737, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:37<04:42, 3.72s/it] 86%|████████▌ | 445/520 [27:40<04:36, 3.69s/it] {'loss': 1.1263, 'grad_norm': 0.0029931999939824504, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:40<04:36, 3.69s/it] 86%|████████▌ | 446/520 [27:44<04:32, 3.68s/it] {'loss': 1.3397, 'grad_norm': 0.002999410324848091, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:44<04:32, 3.68s/it] 86%|████████▌ | 447/520 [27:48<04:28, 3.67s/it] {'loss': 1.213, 'grad_norm': 0.0030494240379497673, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:48<04:28, 3.67s/it] 86%|████████▌ | 448/520 [27:51<04:23, 3.66s/it] {'loss': 1.1932, 'grad_norm': 0.0033535047298120005, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:51<04:23, 3.66s/it] 86%|████████▋ | 449/520 [27:55<04:19, 3.66s/it] {'loss': 1.2921, 'grad_norm': 0.003123030213240878, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:55<04:19, 3.66s/it] 87%|████████▋ | 450/520 [27:59<04:15, 3.64s/it] {'loss': 1.2351, 'grad_norm': 0.0030554952747824443, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:59<04:15, 3.64s/it] 87%|████████▋ | 451/520 [28:02<04:11, 3.65s/it] {'loss': 1.2264, 'grad_norm': 0.0031457864555951534, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:02<04:11, 3.65s/it] 87%|████████▋ | 452/520 [28:06<04:07, 3.64s/it] {'loss': 1.3298, 'grad_norm': 0.0029716836183624664, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:06<04:07, 3.64s/it] 87%|████████▋ | 453/520 [28:10<04:04, 3.64s/it] {'loss': 1.3031, 'grad_norm': 0.003203061139016634, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:10<04:04, 3.64s/it] 87%|████████▋ | 454/520 [28:13<04:00, 3.64s/it] {'loss': 1.1365, 'grad_norm': 0.003310204926725896, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:13<04:00, 3.64s/it] 88%|████████▊ | 455/520 [28:17<03:56, 3.64s/it] {'loss': 1.2736, 'grad_norm': 0.003015303306913062, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:17<03:56, 3.64s/it] 88%|████████▊ | 456/520 [28:20<03:53, 3.64s/it] {'loss': 1.1963, 'grad_norm': 0.0030739460337780235, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:20<03:53, 3.64s/it] 88%|████████▊ | 457/520 [28:24<03:49, 3.64s/it] {'loss': 1.278, 'grad_norm': 0.0029981893228140737, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:24<03:49, 3.64s/it] 88%|████████▊ | 458/520 [28:28<03:46, 3.65s/it] {'loss': 1.3386, 'grad_norm': 0.0032187813078564855, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:28<03:46, 3.65s/it] 88%|████████▊ | 459/520 [28:31<03:42, 3.65s/it] {'loss': 1.2689, 'grad_norm': 0.002978029001380266, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:31<03:42, 3.65s/it] 88%|████████▊ | 460/520 [28:35<03:38, 3.64s/it] {'loss': 1.1401, 'grad_norm': 0.0029556711510098843, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:35<03:38, 3.64s/it] 89%|████████▊ | 461/520 [28:39<03:35, 3.65s/it] {'loss': 1.3511, 'grad_norm': 0.0025317633356672416, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:39<03:35, 3.65s/it] 89%|████████▉ | 462/520 [28:42<03:32, 3.66s/it] {'loss': 1.3773, 'grad_norm': 0.0029733116205948413, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:42<03:32, 3.66s/it] 89%|████████▉ | 463/520 [28:46<03:31, 3.72s/it] {'loss': 1.1053, 'grad_norm': 0.0031621977179683955, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 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520/520 [32:18<00:00, 3.88s/it] 100%|██████████| 520/520 [32:18<00:00, 3.73s/it] +[2025-10-17 10:14:11,407] [INFO] [launch.py:348:main] Process 434837 exits successfully. +[2025-10-17 10:14:11,407] [INFO] [launch.py:348:main] Process 434839 exits successfully. +[2025-10-17 10:14:12,409] [INFO] [launch.py:348:main] Process 434838 exits successfully. +[2025-10-17 10:14:12,410] [INFO] [launch.py:348:main] Process 434842 exits successfully. +[2025-10-17 10:14:12,410] [INFO] [launch.py:348:main] Process 434843 exits successfully. +[2025-10-17 10:14:12,410] [INFO] [launch.py:348:main] Process 434840 exits successfully. +[2025-10-17 10:14:12,411] [INFO] [launch.py:348:main] Process 434841 exits successfully. +[2025-10-17 10:14:16,416] [INFO] [launch.py:348:main] Process 434836 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-1.0_2.9_2e-1_connector-1.0_2.9_2e-1_ablation_20251017_092454.log +Timestamp: 2025-10-17 10:14:18 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation_20251017_101418.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation_20251017_101418.log new file mode 100644 index 0000000000000000000000000000000000000000..a84e1fe2def95b80facd9753100b629bd028e3c1 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation_20251017_101418.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation_20251017_101418.log +Timestamp: 2025-10-17 10:14:18 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 10:14:21,561] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:24,276] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 10:14:24,277] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 3.0 --temperature_attn_text 0.5 --temperature_mlp_text 0.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 3.0 --temperature_attn_vision 0.5 --temperature_mlp_vision 0.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 3.0 --temperature_connector 0.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 10:14:26,834] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:27,882] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 10:14:27,882] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 10:14:27,882] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 10:14:27,882] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 10:14:27,882] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 10:14:27,882] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 10:14:27,882] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 10:14:27,884] [INFO] [launch.py:253:main] process 458463 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,886] [INFO] [launch.py:253:main] process 458464 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,888] [INFO] [launch.py:253:main] process 458465 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,890] [INFO] [launch.py:253:main] process 458466 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,892] [INFO] [launch.py:253:main] process 458467 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,894] [INFO] [launch.py:253:main] process 458468 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,896] [INFO] [launch.py:253:main] process 458469 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 10:14:27,897] [INFO] [launch.py:253:main] process 458470 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '0.5', '--temperature_mlp_text', '0.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '0.5', '--temperature_mlp_vision', '0.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '0.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 10:14:34,573] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:34,735] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:34,925] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:34,965] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:34,983] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:34,984] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,010] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:35,010] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:35,016] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 10:14:35,129] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,332] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,375] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,375] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 10:14:35,392] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,410] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,412] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 10:14:35,423] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 0.5, 'temperature_mlp': 0.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 0.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 0.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 0.5, + "temperature_mlp": 0.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458470:460087 [7] NCCL INFO ncclCommInitRank comm 0x5583c8d68260 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458466:460080 [3] NCCL INFO ncclCommInitRank comm 0x55cfe0335c70 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458464:460081 [1] NCCL INFO ncclCommInitRank comm 0x55746c804770 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458468:460088 [5] NCCL INFO ncclCommInitRank comm 0x555620be8c00 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458469:460083 [6] NCCL INFO ncclCommInitRank comm 0x55a286af2530 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458465:460079 [2] NCCL INFO ncclCommInitRank comm 0x56167260e680 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458463:460078 [0] NCCL INFO ncclCommInitRank comm 0x55f6e549bd20 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xcc6a72321495cc42 - Init COMPLETE +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:458467:460082 [4] NCCL INFO ncclCommInitRank comm 0x557158e32a00 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xcc6a72321495cc42 - Init COMPLETE +[2025-10-17 10:15:21,788] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 10:15:23,484] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=3.000000 +Pre-training init connector._connector.0.scores: Mean=3.000005 +Pre-training init connector._connector.2.scores: Mean=2.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 10:15:41,278 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 10:15:41,285 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:005->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read 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22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Connected all rings 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06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458464:465127 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458470:465131 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458465:465128 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458467:465129 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458469:465130 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:458468:465133 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:458466:465132 [3] NCCL INFO ncclCommInitRank comm 0x7fddb806aaa0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x66e342592d510b6c - Init COMPLETE 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0x7f188406a860 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x66e342592d510b6c - Init COMPLETE +ywang29-vrdb-test1-worker-0:458463:465126 [0] NCCL INFO ncclCommInitRank comm 0x7f7af006af70 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x66e342592d510b6c - Init COMPLETE + 0%| | 1/520 [00:14<2:09:00, 14.92s/it] {'loss': 2.0453, 'grad_norm': 0.004835120713275944, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:09:00, 14.92s/it] 0%| | 2/520 [00:18<1:11:26, 8.27s/it] {'loss': 2.0549, 'grad_norm': 0.005248916820203103, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:11:26, 8.27s/it] 1%| | 3/520 [00:22<52:56, 6.14s/it] {'loss': 2.1899, 'grad_norm': 0.006007344528584803, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<52:56, 6.14s/it] 1%| | 4/520 [00:25<44:28, 5.17s/it] {'loss': 2.0656, 'grad_norm': 0.004964256556214463, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<44:28, 5.17s/it] 1%| | 5/520 [00:29<39:34, 4.61s/it] {'loss': 2.2333, 'grad_norm': 0.005481552095575976, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<39:34, 4.61s/it] 1%| | 6/520 [00:33<36:41, 4.28s/it] {'loss': 1.4706, 'grad_norm': 0.001653293540684356, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<36:41, 4.28s/it] 1%|▏ | 7/520 [00:36<34:43, 4.06s/it] {'loss': 1.5581, 'grad_norm': 0.000970285918658136, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<34:43, 4.06s/it] 2%|▏ | 8/520 [00:40<35:13, 4.13s/it] {'loss': 1.5606, 'grad_norm': 0.0006642631118085748, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<35:13, 4.13s/it] 2%|▏ | 9/520 [00:45<35:31, 4.17s/it] {'loss': 1.6219, 'grad_norm': 0.0005795223925040548, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:45<35:31, 4.17s/it] 2%|▏ | 10/520 [00:48<34:19, 4.04s/it] {'loss': 1.4748, 'grad_norm': 0.0005804778273218297, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<34:19, 4.04s/it] 2%|▏ | 11/520 [00:52<33:48, 3.98s/it] {'loss': 1.4828, 'grad_norm': 0.0004968994726903929, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:52<33:48, 3.98s/it] 2%|▏ | 12/520 [00:56<33:15, 3.93s/it] {'loss': 1.3547, 'grad_norm': 0.00048386288841528464, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:56<33:15, 3.93s/it][2025-10-17 10:16:46,824] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. 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0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:15<31:23, 3.75s/it] 3%|▎ | 18/520 [01:19<30:56, 3.70s/it] {'loss': 1.343, 'grad_norm': 0.000900152328932538, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:19<30:56, 3.70s/it] 4%|▎ | 19/520 [01:22<30:42, 3.68s/it] {'loss': 1.3406, 'grad_norm': 0.0008851123951679571, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:22<30:42, 3.68s/it] 4%|▍ | 20/520 [01:26<30:30, 3.66s/it] {'loss': 1.3104, 'grad_norm': 0.001000845719258513, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:26<30:30, 3.66s/it] 4%|▍ | 21/520 [01:30<30:24, 3.66s/it] {'loss': 1.3232, 'grad_norm': 0.0011540878924268536, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:30<30:24, 3.66s/it] 4%|▍ | 22/520 [01:33<30:11, 3.64s/it] {'loss': 1.4428, 'grad_norm': 0.0011345259874292303, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:33<30:11, 3.64s/it] 4%|▍ | 23/520 [01:37<30:07, 3.64s/it] {'loss': 1.3973, 'grad_norm': 0.0014816721299708495, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:37<30:07, 3.64s/it] 5%|▍ | 24/520 [01:40<29:57, 3.62s/it] {'loss': 1.3086, 'grad_norm': 0.0011997244841665546, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<29:57, 3.62s/it] 5%|▍ | 25/520 [01:44<29:52, 3.62s/it] {'loss': 1.3907, 'grad_norm': 0.0015687541900546902, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:44<29:52, 3.62s/it] 5%|▌ | 26/520 [01:48<29:51, 3.63s/it] {'loss': 1.3285, 'grad_norm': 0.0011131023958005765, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<29:51, 3.63s/it] 5%|▌ | 27/520 [01:51<29:46, 3.62s/it] {'loss': 1.2694, 'grad_norm': 0.0013664791599387722, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:51<29:46, 3.62s/it] 5%|▌ | 28/520 [01:55<29:44, 3.63s/it] {'loss': 1.291, 'grad_norm': 0.0014346374528427172, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:55<29:44, 3.63s/it] 6%|▌ | 29/520 [01:59<29:38, 3.62s/it] {'loss': 1.3094, 'grad_norm': 0.0014675055361577018, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:59<29:38, 3.62s/it] 6%|▌ | 30/520 [02:02<29:41, 3.64s/it] {'loss': 1.3787, 'grad_norm': 0.00127959790501696, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:02<29:41, 3.64s/it] 6%|▌ | 31/520 [02:06<29:38, 3.64s/it] {'loss': 1.2796, 'grad_norm': 0.001342446006724984, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:06<29:38, 3.64s/it] 6%|▌ | 32/520 [02:10<29:38, 3.64s/it] {'loss': 1.2159, 'grad_norm': 0.0014918223414830069, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:10<29:38, 3.64s/it] 6%|▋ | 33/520 [02:13<29:41, 3.66s/it] {'loss': 1.2843, 'grad_norm': 0.0016944164309095303, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:13<29:41, 3.66s/it] 7%|▋ | 34/520 [02:17<29:38, 3.66s/it] {'loss': 1.2806, 'grad_norm': 0.0017518054374260302, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:17<29:38, 3.66s/it] 7%|▋ | 35/520 [02:21<29:32, 3.65s/it] {'loss': 1.2833, 'grad_norm': 0.001862806753648716, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:21<29:32, 3.65s/it] 7%|▋ | 36/520 [02:24<29:24, 3.65s/it] {'loss': 1.3829, 'grad_norm': 0.0016237349427498477, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:24<29:24, 3.65s/it] 7%|▋ | 37/520 [02:28<29:24, 3.65s/it] {'loss': 1.3666, 'grad_norm': 0.001563628952744469, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:28<29:24, 3.65s/it] 7%|▋ | 38/520 [02:31<29:21, 3.66s/it] {'loss': 1.4598, 'grad_norm': 0.0017288073952123316, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:31<29:21, 3.66s/it] 8%|▊ | 39/520 [02:35<29:12, 3.64s/it] {'loss': 1.323, 'grad_norm': 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{'loss': 1.3691, 'grad_norm': 0.0019103505543729936, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:15<28:24, 3.63s/it] 10%|▉ | 51/520 [03:19<28:23, 3.63s/it] {'loss': 1.2971, 'grad_norm': 0.0019995795938508617, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:19<28:23, 3.63s/it] 10%|█ | 52/520 [03:22<28:16, 3.63s/it] {'loss': 1.4331, 'grad_norm': 0.002278380214919828, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:22<28:16, 3.63s/it] 10%|█ | 53/520 [03:26<28:15, 3.63s/it] {'loss': 1.4006, 'grad_norm': 0.0018625490225766264, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:26<28:15, 3.63s/it] 10%|█ | 54/520 [03:30<28:12, 3.63s/it] {'loss': 1.3332, 'grad_norm': 0.0017782319039934993, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:30<28:12, 3.63s/it] 11%|█ | 55/520 [03:33<28:13, 3.64s/it] {'loss': 1.3031, 'grad_norm': 0.0019724913019946726, 'learning_rate': 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0.0008819207254393191, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:38<11:11, 3.59s/it] 64%|██████▍ | 334/520 [20:42<11:07, 3.59s/it] {'loss': 1.2201, 'grad_norm': 0.0008958929334068153, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:42<11:07, 3.59s/it] 64%|██████▍ | 335/520 [20:45<11:04, 3.59s/it] {'loss': 1.2173, 'grad_norm': 0.0007740338357019048, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:45<11:04, 3.59s/it] 65%|██████▍ | 336/520 [20:49<11:00, 3.59s/it] {'loss': 1.1174, 'grad_norm': 0.0008784452721003038, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:49<11:00, 3.59s/it] 65%|██████▍ | 337/520 [20:53<10:57, 3.59s/it] {'loss': 1.112, 'grad_norm': 0.0008035713223419105, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:53<10:57, 3.59s/it] 65%|██████▌ | 338/520 [20:56<10:55, 3.60s/it] {'loss': 1.2284, 'grad_norm': 0.0008283979646463532, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [20:56<10:55, 3.60s/it] 65%|██████▌ | 339/520 [21:00<10:52, 3.60s/it] {'loss': 1.1763, 'grad_norm': 0.0008434890665431264, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:00<10:52, 3.60s/it] 65%|██████▌ | 340/520 [21:03<10:50, 3.61s/it] {'loss': 1.1568, 'grad_norm': 0.0007899258383271299, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:03<10:50, 3.61s/it] 66%|██████▌ | 341/520 [21:07<10:47, 3.62s/it] {'loss': 1.1887, 'grad_norm': 0.0009029200685108083, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:07<10:47, 3.62s/it] 66%|██████▌ | 342/520 [21:11<10:43, 3.61s/it] {'loss': 1.2321, 'grad_norm': 0.0009999548890194908, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:11<10:43, 3.61s/it] 66%|██████▌ | 343/520 [21:14<10:39, 3.62s/it] {'loss': 1.186, 'grad_norm': 0.0007097667900401972, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:14<10:39, 3.62s/it] 66%|██████▌ | 344/520 [21:18<10:36, 3.61s/it] {'loss': 1.1393, 'grad_norm': 0.0007373884280002834, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:18<10:36, 3.61s/it] 66%|██████▋ | 345/520 [21:22<10:34, 3.62s/it] {'loss': 1.2479, 'grad_norm': 0.0008348454185784426, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:22<10:34, 3.62s/it] 67%|██████▋ | 346/520 [21:25<10:30, 3.62s/it] {'loss': 1.196, 'grad_norm': 0.0008350872315308707, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:25<10:30, 3.62s/it] 67%|██████▋ | 347/520 [21:29<10:27, 3.63s/it] {'loss': 1.1588, 'grad_norm': 0.000749361970498135, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:29<10:27, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:32<10:22, 3.62s/it] {'loss': 1.1197, 'grad_norm': 0.0009797494968904728, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:32<10:22, 3.62s/it] 67%|██████▋ | 349/520 [21:36<10:19, 3.62s/it] {'loss': 1.155, 'grad_norm': 0.0008397182349570964, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:36<10:19, 3.62s/it] 67%|██████▋ | 350/520 [21:40<10:15, 3.62s/it] {'loss': 1.1972, 'grad_norm': 0.0008258831773373475, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:40<10:15, 3.62s/it] 68%|██████▊ | 351/520 [21:43<10:12, 3.63s/it] {'loss': 1.1053, 'grad_norm': 0.0007866096812365246, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:43<10:12, 3.63s/it] 68%|██████▊ | 352/520 [21:47<10:08, 3.62s/it] {'loss': 1.2251, 'grad_norm': 0.0007744274415641479, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:47<10:08, 3.62s/it] 68%|██████▊ | 353/520 [21:51<10:09, 3.65s/it] {'loss': 1.1646, 'grad_norm': 0.0007008685616131036, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:51<10:09, 3.65s/it] 68%|██████▊ | 354/520 [21:54<10:14, 3.70s/it] {'loss': 1.2738, 'grad_norm': 0.0007978439370751818, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:54<10:14, 3.70s/it] 68%|██████▊ | 355/520 [21:58<10:16, 3.74s/it] {'loss': 1.1683, 'grad_norm': 0.0008085427536601936, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [21:58<10:16, 3.74s/it] 68%|██████▊ | 356/520 [22:02<10:17, 3.77s/it] {'loss': 1.1706, 'grad_norm': 0.0008170305522070814, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:02<10:17, 3.77s/it] 69%|██████▊ | 357/520 [22:06<10:16, 3.78s/it] {'loss': 1.2012, 'grad_norm': 0.0007850791545323087, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:06<10:16, 3.78s/it] 69%|██████▉ | 358/520 [22:10<10:18, 3.82s/it] {'loss': 1.129, 'grad_norm': 0.0008003260512869316, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:10<10:18, 3.82s/it] 69%|██████▉ | 359/520 [22:14<10:15, 3.82s/it] {'loss': 1.2099, 'grad_norm': 0.000890264539427017, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:14<10:15, 3.82s/it] 69%|██████▉ | 360/520 [22:17<10:08, 3.80s/it] {'loss': 1.2248, 'grad_norm': 0.0008479963667785171, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:17<10:08, 3.80s/it] 69%|██████▉ | 361/520 [22:21<10:02, 3.79s/it] {'loss': 1.2243, 'grad_norm': 0.0007399072997901874, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:21<10:02, 3.79s/it] 70%|██████▉ | 362/520 [22:25<09:56, 3.78s/it] {'loss': 1.1798, 'grad_norm': 0.0008743681146200018, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:25<09:56, 3.78s/it] 70%|██████▉ | 363/520 [22:29<09:51, 3.77s/it] {'loss': 1.2234, 'grad_norm': 0.0008244462531620616, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:29<09:51, 3.77s/it] 70%|███████ | 364/520 [22:32<09:47, 3.77s/it] {'loss': 1.252, 'grad_norm': 0.0008374058332771249, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:32<09:47, 3.77s/it] 70%|███████ | 365/520 [22:36<09:42, 3.76s/it] {'loss': 1.2642, 'grad_norm': 0.0008139648815443714, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:36<09:42, 3.76s/it] 70%|███████ | 366/520 [22:40<09:39, 3.76s/it] {'loss': 1.2277, 'grad_norm': 0.0008083009328454442, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:40<09:39, 3.76s/it] 71%|███████ | 367/520 [22:44<09:35, 3.76s/it] {'loss': 1.2261, 'grad_norm': 0.0008346675530766688, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:44<09:35, 3.76s/it] 71%|███████ | 368/520 [22:47<09:24, 3.72s/it] {'loss': 1.0817, 'grad_norm': 0.0008256684195030427, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:47<09:24, 3.72s/it] 71%|███████ | 369/520 [22:51<09:15, 3.68s/it] {'loss': 1.2012, 'grad_norm': 0.0007371813027400674, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:51<09:15, 3.68s/it] 71%|███████ | 370/520 [22:54<09:08, 3.65s/it] {'loss': 1.1419, 'grad_norm': 0.0007733715787087517, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:54<09:08, 3.65s/it] 71%|███████▏ | 371/520 [22:58<09:01, 3.63s/it] {'loss': 1.139, 'grad_norm': 0.0008655669066996657, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [22:58<09:01, 3.63s/it] 72%|███████▏ | 372/520 [23:02<08:56, 3.62s/it] {'loss': 1.2834, 'grad_norm': 0.0007501114296321793, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:02<08:56, 3.62s/it] 72%|███████▏ | 373/520 [23:05<08:56, 3.65s/it] {'loss': 1.1683, 'grad_norm': 0.0008696418429772932, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:05<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:09<08:59, 3.69s/it] {'loss': 1.23, 'grad_norm': 0.0008502944336825584, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:09<08:59, 3.69s/it] 72%|███████▏ | 375/520 [23:13<08:59, 3.72s/it] {'loss': 1.1405, 'grad_norm': 0.0008010411723062696, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:13<08:59, 3.72s/it] 72%|███████▏ | 376/520 [23:17<08:58, 3.74s/it] {'loss': 1.2477, 'grad_norm': 0.0007807955563761902, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:17<08:58, 3.74s/it] 72%|███████▎ | 377/520 [23:20<08:56, 3.75s/it] {'loss': 1.1898, 'grad_norm': 0.0008670632525792065, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:20<08:56, 3.75s/it] 73%|███████▎ | 378/520 [23:24<08:53, 3.76s/it] {'loss': 1.2473, 'grad_norm': 0.000784792040465654, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:24<08:53, 3.76s/it] 73%|███████▎ | 379/520 [23:28<08:50, 3.76s/it] {'loss': 1.2174, 'grad_norm': 0.0007868079481333657, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:28<08:50, 3.76s/it] 73%|███████▎ | 380/520 [23:32<08:46, 3.76s/it] {'loss': 1.2559, 'grad_norm': 0.0008389163757293434, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:32<08:46, 3.76s/it] 73%|███████▎ | 381/520 [23:36<08:45, 3.78s/it] {'loss': 1.2231, 'grad_norm': 0.0007875030604112129, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:36<08:45, 3.78s/it] 73%|███████▎ | 382/520 [23:39<08:42, 3.79s/it] {'loss': 1.2207, 'grad_norm': 0.0007678515912856045, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:39<08:42, 3.79s/it] 74%|███████▎ | 383/520 [23:43<08:38, 3.78s/it] {'loss': 1.0637, 'grad_norm': 0.0008917741199904503, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:43<08:38, 3.78s/it] 74%|███████▍ | 384/520 [23:47<08:36, 3.80s/it] {'loss': 1.2691, 'grad_norm': 0.00074567151681843, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:47<08:36, 3.80s/it] 74%|███████▍ | 385/520 [23:51<08:32, 3.80s/it] {'loss': 1.2043, 'grad_norm': 0.0007595235369306475, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:51<08:32, 3.80s/it] 74%|███████▍ | 386/520 [23:54<08:22, 3.75s/it] {'loss': 1.1559, 'grad_norm': 0.0007176559035957934, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:54<08:22, 3.75s/it] 74%|███████▍ | 387/520 [23:58<08:11, 3.70s/it] {'loss': 1.282, 'grad_norm': 0.0008139528608406879, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [23:58<08:11, 3.70s/it] 75%|███████▍ | 388/520 [24:02<08:03, 3.66s/it] {'loss': 1.1094, 'grad_norm': 0.0007696849836611386, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:02<08:03, 3.66s/it] 75%|███████▍ | 389/520 [24:05<07:56, 3.64s/it] {'loss': 1.1536, 'grad_norm': 0.0009137002807519148, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:05<07:56, 3.64s/it] 75%|███████▌ | 390/520 [24:09<07:50, 3.62s/it] {'loss': 1.2341, 'grad_norm': 0.000819561688143587, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:09<07:50, 3.62s/it] 75%|███████▌ | 391/520 [24:12<07:46, 3.61s/it] {'loss': 1.2971, 'grad_norm': 0.0008339721598541421, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:12<07:46, 3.61s/it] 75%|███████▌ | 392/520 [24:16<07:42, 3.61s/it] {'loss': 1.1213, 'grad_norm': 0.0008133943363633716, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:16<07:42, 3.61s/it] 76%|███████▌ | 393/520 [24:20<07:37, 3.60s/it] {'loss': 1.1275, 'grad_norm': 0.0007249654453725795, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:20<07:37, 3.60s/it] 76%|███████▌ | 394/520 [24:23<07:34, 3.61s/it] {'loss': 1.1844, 'grad_norm': 0.0008509089351707248, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:23<07:34, 3.61s/it] 76%|███████▌ | 395/520 [24:27<07:34, 3.64s/it] {'loss': 1.151, 'grad_norm': 0.0008829557268443177, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:27<07:34, 3.64s/it] 76%|███████▌ | 396/520 [24:31<07:30, 3.64s/it] {'loss': 1.2355, 'grad_norm': 0.000897263825784078, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:31<07:30, 3.64s/it] 76%|███████▋ | 397/520 [24:34<07:27, 3.64s/it] {'loss': 1.2037, 'grad_norm': 0.0008228408693873826, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:34<07:27, 3.64s/it] 77%|███████▋ | 398/520 [24:38<07:24, 3.64s/it] {'loss': 1.1998, 'grad_norm': 0.0008628867527148376, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:38<07:24, 3.64s/it] 77%|███████▋ | 399/520 [24:42<07:22, 3.66s/it] {'loss': 1.1661, 'grad_norm': 0.0007905633521777802, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:42<07:22, 3.66s/it] 77%|███████▋ | 400/520 [24:45<07:18, 3.66s/it] {'loss': 1.1963, 'grad_norm': 0.0007448529919139421, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:45<07:18, 3.66s/it] 77%|███████▋ | 401/520 [24:49<07:14, 3.65s/it] {'loss': 1.0406, 'grad_norm': 0.0008779533898615263, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:49<07:14, 3.65s/it] 77%|███████▋ | 402/520 [24:52<07:10, 3.65s/it] {'loss': 1.1643, 'grad_norm': 0.0008248517308490317, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:52<07:10, 3.65s/it] 78%|███████▊ | 403/520 [24:56<07:05, 3.64s/it] {'loss': 1.1926, 'grad_norm': 0.0009067093436054134, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [24:56<07:05, 3.64s/it] 78%|███████▊ | 404/520 [25:00<07:00, 3.63s/it] {'loss': 1.097, 'grad_norm': 0.000935895735031891, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:00<07:00, 3.63s/it] 78%|███████▊ | 405/520 [25:03<06:57, 3.63s/it] {'loss': 1.1783, 'grad_norm': 0.000786041936523933, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:03<06:57, 3.63s/it] 78%|███████▊ | 406/520 [25:07<06:53, 3.63s/it] {'loss': 1.1034, 'grad_norm': 0.0009662604030878089, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:07<06:53, 3.63s/it] 78%|███████▊ | 407/520 [25:11<06:49, 3.62s/it] {'loss': 1.279, 'grad_norm': 0.0008442268331339955, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:11<06:49, 3.62s/it] 78%|███████▊ | 408/520 [25:14<06:44, 3.61s/it] {'loss': 1.1798, 'grad_norm': 0.0009103480995476037, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:14<06:44, 3.61s/it] 79%|███████▊ | 409/520 [25:18<06:41, 3.62s/it] {'loss': 1.3036, 'grad_norm': 0.0008922101294856812, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:18<06:41, 3.62s/it] 79%|███████▉ | 410/520 [25:21<06:38, 3.62s/it] {'loss': 1.0374, 'grad_norm': 0.0008268417962155027, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:21<06:38, 3.62s/it] 79%|███████▉ | 411/520 [25:25<06:34, 3.62s/it] {'loss': 1.2816, 'grad_norm': 0.0009051206736654235, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:25<06:34, 3.62s/it] 79%|███████▉ | 412/520 [25:29<06:32, 3.63s/it] {'loss': 1.1892, 'grad_norm': 0.0008402802134088181, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:29<06:32, 3.63s/it] 79%|███████▉ | 413/520 [25:32<06:29, 3.64s/it] {'loss': 1.1996, 'grad_norm': 0.0008834750400601782, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:32<06:29, 3.64s/it] 80%|███████▉ | 414/520 [25:36<06:25, 3.64s/it] {'loss': 1.0029, 'grad_norm': 0.0007013898245850039, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:36<06:25, 3.64s/it] 80%|███████▉ | 415/520 [25:40<06:22, 3.64s/it] {'loss': 1.1734, 'grad_norm': 0.0007670700743156128, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:40<06:22, 3.64s/it] 80%|████████ | 416/520 [25:43<06:18, 3.64s/it] {'loss': 1.0876, 'grad_norm': 0.0008915175848328129, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:43<06:18, 3.64s/it] 80%|████████ | 417/520 [25:47<06:15, 3.64s/it] {'loss': 1.242, 'grad_norm': 0.0008323430008137083, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:47<06:15, 3.64s/it] 80%|████████ | 418/520 [25:51<06:11, 3.64s/it] {'loss': 1.2361, 'grad_norm': 0.0007634415041624106, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:51<06:11, 3.64s/it] 81%|████████ | 419/520 [25:54<06:07, 3.63s/it] {'loss': 1.2264, 'grad_norm': 0.0009489886792419266, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:54<06:07, 3.63s/it] 81%|████████ | 420/520 [25:58<06:04, 3.65s/it] {'loss': 1.1188, 'grad_norm': 0.0008497379067049803, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [25:58<06:04, 3.65s/it] 81%|████████ | 421/520 [26:01<06:00, 3.64s/it] {'loss': 1.0532, 'grad_norm': 0.000830099917146354, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:01<06:00, 3.64s/it] 81%|████████ | 422/520 [26:05<05:55, 3.63s/it] {'loss': 1.1724, 'grad_norm': 0.000877315775008156, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:05<05:55, 3.63s/it] 81%|████████▏ | 423/520 [26:09<05:51, 3.62s/it] {'loss': 1.1444, 'grad_norm': 0.0009075639857121242, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:09<05:51, 3.62s/it] 82%|████████▏ | 424/520 [26:12<05:47, 3.62s/it] {'loss': 1.2845, 'grad_norm': 0.0008053336906470188, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:12<05:47, 3.62s/it] 82%|████████▏ | 425/520 [26:16<05:43, 3.61s/it] {'loss': 1.1692, 'grad_norm': 0.0008119658372137879, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:16<05:43, 3.61s/it] 82%|████████▏ | 426/520 [26:19<05:38, 3.60s/it] {'loss': 1.1947, 'grad_norm': 0.0010389022340489152, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:19<05:38, 3.60s/it] 82%|████████▏ | 427/520 [26:23<05:34, 3.60s/it] {'loss': 1.101, 'grad_norm': 0.000800964701403522, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:23<05:34, 3.60s/it] 82%|████████▏ | 428/520 [26:27<05:30, 3.59s/it] {'loss': 1.0863, 'grad_norm': 0.0008768391437318506, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:27<05:30, 3.59s/it] 82%|████████▎ | 429/520 [26:30<05:27, 3.60s/it] {'loss': 1.1848, 'grad_norm': 0.0008223713624404758, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:30<05:27, 3.60s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:34<05:24, 3.60s/it] {'loss': 1.1764, 'grad_norm': 0.0007780918358040095, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:34<05:24, 3.60s/it] 83%|████████▎ | 431/520 [26:37<05:20, 3.60s/it] {'loss': 1.1704, 'grad_norm': 0.0008404313841058984, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:37<05:20, 3.60s/it] 83%|████████▎ | 432/520 [26:41<05:19, 3.63s/it] {'loss': 1.0905, 'grad_norm': 0.0008460475030050544, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:41<05:19, 3.63s/it] 83%|████████▎ | 433/520 [26:45<05:20, 3.69s/it] {'loss': 1.2277, 'grad_norm': 0.0008171519857868547, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:45<05:20, 3.69s/it] 83%|████████▎ | 434/520 [26:49<05:21, 3.74s/it] {'loss': 0.9682, 'grad_norm': 0.0008225346521886011, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:49<05:21, 3.74s/it] 84%|████████▎ | 435/520 [26:53<05:19, 3.76s/it] {'loss': 1.2619, 'grad_norm': 0.0009102856240423174, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:53<05:19, 3.76s/it] 84%|████████▍ | 436/520 [26:56<05:17, 3.78s/it] {'loss': 1.0601, 'grad_norm': 0.0008504994502585025, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:56<05:17, 3.78s/it] 84%|████████▍ | 437/520 [27:00<05:13, 3.78s/it] {'loss': 1.2868, 'grad_norm': 0.0008478357318903063, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:00<05:13, 3.78s/it] 84%|████████▍ | 438/520 [27:04<05:05, 3.73s/it] {'loss': 1.0969, 'grad_norm': 0.0008187084268281865, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:04<05:05, 3.73s/it] 84%|████████▍ | 439/520 [27:07<04:59, 3.70s/it] {'loss': 1.1494, 'grad_norm': 0.0006937999271802029, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:07<04:59, 3.70s/it] 85%|████████▍ | 440/520 [27:11<04:53, 3.67s/it] {'loss': 1.1374, 'grad_norm': 0.0008651594958865448, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:11<04:53, 3.67s/it] 85%|████████▍ | 441/520 [27:15<04:48, 3.65s/it] {'loss': 1.1701, 'grad_norm': 0.0007859926544685277, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:15<04:48, 3.65s/it] 85%|████████▌ | 442/520 [27:18<04:43, 3.63s/it] {'loss': 1.1963, 'grad_norm': 0.0009244498477926922, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:18<04:43, 3.63s/it] 85%|████████▌ | 443/520 [27:22<04:38, 3.62s/it] {'loss': 1.2104, 'grad_norm': 0.0008014308467038377, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:22<04:38, 3.62s/it] 85%|████████▌ | 444/520 [27:25<04:35, 3.62s/it] {'loss': 1.1777, 'grad_norm': 0.0007494361810419224, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:25<04:35, 3.62s/it] 86%|████████▌ | 445/520 [27:29<04:31, 3.62s/it] {'loss': 1.1076, 'grad_norm': 0.0008132935999010035, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:29<04:31, 3.62s/it] 86%|████████▌ | 446/520 [27:33<04:27, 3.62s/it] {'loss': 1.2466, 'grad_norm': 0.0007622443077842981, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:33<04:27, 3.62s/it] 86%|████████▌ | 447/520 [27:36<04:24, 3.62s/it] {'loss': 1.1815, 'grad_norm': 0.0008100821239223787, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:36<04:24, 3.62s/it] 86%|████████▌ | 448/520 [27:40<04:20, 3.62s/it] {'loss': 1.1679, 'grad_norm': 0.0009487577676552824, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:40<04:20, 3.62s/it] 86%|████████▋ | 449/520 [27:44<04:16, 3.61s/it] {'loss': 1.2038, 'grad_norm': 0.000840849470453014, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:44<04:16, 3.61s/it] 87%|████████▋ | 450/520 [27:47<04:12, 3.61s/it] {'loss': 1.2025, 'grad_norm': 0.0008431127992534161, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:47<04:12, 3.61s/it] 87%|████████▋ | 451/520 [27:51<04:08, 3.60s/it] {'loss': 1.2021, 'grad_norm': 0.0008333900442770239, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:51<04:08, 3.60s/it] 87%|████████▋ | 452/520 [27:54<04:04, 3.60s/it] {'loss': 1.2481, 'grad_norm': 0.0007813318072122003, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:54<04:04, 3.60s/it] 87%|████████▋ | 453/520 [27:58<04:01, 3.60s/it] {'loss': 1.2235, 'grad_norm': 0.0007807518353423092, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [27:58<04:01, 3.60s/it] 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100%|██████████| 520/520 [32:11<00:00, 4.07s/it] 100%|██████████| 520/520 [32:11<00:00, 3.72s/it] +[2025-10-17 10:48:03,043] [INFO] [launch.py:348:main] Process 458469 exits successfully. +[2025-10-17 10:48:04,045] [INFO] [launch.py:348:main] Process 458464 exits successfully. +[2025-10-17 10:48:04,045] [INFO] [launch.py:348:main] Process 458466 exits successfully. +[2025-10-17 10:48:04,046] [INFO] [launch.py:348:main] Process 458470 exits successfully. +[2025-10-17 10:48:04,046] [INFO] [launch.py:348:main] Process 458467 exits successfully. +[2025-10-17 10:48:05,048] [INFO] [launch.py:348:main] Process 458468 exits successfully. +[2025-10-17 10:48:05,048] [INFO] [launch.py:348:main] Process 458465 exits successfully. +[2025-10-17 10:48:08,052] [INFO] [launch.py:348:main] Process 458463 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_0.5_2e-1_connector-3.0_0.5_2e-1_ablation_20251017_101418.log +Timestamp: 2025-10-17 10:48:10 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation_20251017_161830.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation_20251017_161830.log new file mode 100644 index 0000000000000000000000000000000000000000..fe50a7f53a45062c9a4c5fe33680b42e1ef34282 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation_20251017_161830.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation_20251017_161830.log +Timestamp: 2025-10-17 16:18:30 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:18:32,970] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:36,295] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 16:18:36,297] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 3.0 --temperature_attn_text 2.3 --temperature_mlp_text 2.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 3.0 --temperature_attn_vision 2.3 --temperature_mlp_vision 2.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 3.0 --temperature_connector 2.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:18:38,906] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:39,974] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 16:18:39,974] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 16:18:39,974] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 16:18:39,974] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 16:18:39,975] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 16:18:39,975] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 16:18:39,975] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 16:18:39,977] [INFO] [launch.py:253:main] process 482269 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,979] [INFO] [launch.py:253:main] process 482270 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,981] [INFO] [launch.py:253:main] process 482271 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,982] [INFO] [launch.py:253:main] process 482272 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,984] [INFO] [launch.py:253:main] process 482273 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,986] [INFO] [launch.py:253:main] process 482274 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,988] [INFO] [launch.py:253:main] process 482275 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:18:39,990] [INFO] [launch.py:253:main] process 482276 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '3.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '3.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '3.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:18:47,030] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,030] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,082] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,083] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,090] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,094] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,108] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,111] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:18:47,632] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.3, 'temperature_mlp': 2.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.3, + "temperature_mlp": 2.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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+ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO NVLS multicast support is not available on dev 2 +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO comm 0x55a2283ea810 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO comm 0x56423f1f20e0 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO comm 0x55be3f1431a0 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO comm 0x559997097780 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO comm 0x55d452943eb0 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO comm 0x558dd0692370 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO comm 0x5594b39f4d80 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO comm 0x56418ddb4180 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 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NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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[0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482271:483970 [2] NCCL INFO ncclCommInitRank comm 0x56423f1f20e0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482270:483969 [1] NCCL INFO ncclCommInitRank comm 0x55be3f1431a0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482269:483946 [0] NCCL INFO ncclCommInitRank comm 0x559997097780 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482272:483971 [3] NCCL INFO ncclCommInitRank comm 0x55a2283ea810 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482274:483972 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:482273:483967 [4] NCCL INFO ncclCommInitRank comm 0x56418ddb4180 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482276:483973 [7] NCCL INFO ncclCommInitRank comm 0x55d452943eb0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x9fab967033bde506 - Init COMPLETE +ywang29-vrdb-test1-worker-0:482275:483968 [6] NCCL INFO ncclCommInitRank comm 0x5594b39f4d80 rank 6 nranks 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'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 16:19:41,024] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=3.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=3.000000 +Pre-training init connector._connector.0.scores: Mean=3.000005 +Pre-training init connector._connector.2.scores: Mean=2.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 16:19:59,195 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 16:19:59,201 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:004->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482269:489027 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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+ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482271:489029 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482275:489030 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:482273:489034 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xb48f8ef12b14ec1c - Init COMPLETE +ywang29-vrdb-test1-worker-0:482276:489028 [7] NCCL INFO ncclCommInitRank comm 0x7fa8d406af00 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xb48f8ef12b14ec1c - Init COMPLETE +ywang29-vrdb-test1-worker-0:482272:489031 [3] NCCL INFO ncclCommInitRank comm 0x7f743006b900 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xb48f8ef12b14ec1c - Init COMPLETE +ywang29-vrdb-test1-worker-0:482274:489033 [5] NCCL INFO ncclCommInitRank comm 0x7f5d2006b140 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xb48f8ef12b14ec1c - Init COMPLETE +ywang29-vrdb-test1-worker-0:482270:489032 [1] NCCL INFO ncclCommInitRank comm 0x7f4abc06ac40 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xb48f8ef12b14ec1c - Init COMPLETE + 0%| | 1/520 [00:13<1:57:53, 13.63s/it] {'loss': 3.8836, 'grad_norm': 0.258413480269408, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:13<1:57:53, 13.63s/it] 0%| | 2/520 [00:17<1:06:49, 7.74s/it] {'loss': 3.5651, 'grad_norm': 0.2395843844866327, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:06:49, 7.74s/it] 1%| | 3/520 [00:20<50:21, 5.84s/it] {'loss': 2.136, 'grad_norm': 0.027045874552872745, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:20<50:21, 5.84s/it] 1%| | 4/520 [00:24<42:38, 4.96s/it] {'loss': 1.9022, 'grad_norm': 0.016274831136859114, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:24<42:38, 4.96s/it] 1%| | 5/520 [00:28<38:27, 4.48s/it] {'loss': 1.9088, 'grad_norm': 0.021141408825577333, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<38:27, 4.48s/it] 1%| | 6/520 [00:31<35:51, 4.19s/it] {'loss': 1.7351, 'grad_norm': 0.015331617567761496, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:31<35:51, 4.19s/it] 1%|▏ | 7/520 [00:35<34:05, 3.99s/it] {'loss': 1.6005, 'grad_norm': 0.013194462026616349, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:35<34:05, 3.99s/it] 2%|▏ | 8/520 [00:39<34:38, 4.06s/it] {'loss': 1.6208, 'grad_norm': 0.00839458368113431, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:39<34:38, 4.06s/it] 2%|▏ | 9/520 [00:43<34:48, 4.09s/it] {'loss': 1.6805, 'grad_norm': 0.007537132585236485, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:43<34:48, 4.09s/it] 2%|▏ | 10/520 [00:47<33:26, 3.93s/it] {'loss': 1.4866, 'grad_norm': 0.006416279368143323, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:47<33:26, 3.93s/it] 2%|▏ | 11/520 [00:50<32:49, 3.87s/it] {'loss': 1.5794, 'grad_norm': 0.006595335980353283, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:50<32:49, 3.87s/it] 2%|▏ | 12/520 [00:54<32:08, 3.80s/it] {'loss': 1.5393, 'grad_norm': 0.00510921153946227, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:54<32:08, 3.80s/it][2025-10-17 16:21:02,971] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:58<33:21, 3.95s/it] {'loss': 1.5132, 'grad_norm': 0.004917706992975643, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:58<33:21, 3.95s/it] 3%|▎ | 14/520 [01:02<32:27, 3.85s/it] {'loss': 1.5538, 'grad_norm': 0.004700831522920823, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:02<32:27, 3.85s/it] 3%|▎ | 15/520 [01:06<31:42, 3.77s/it] {'loss': 1.5797, 'grad_norm': 0.0054764708363949114, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:06<31:42, 3.77s/it] 3%|▎ | 16/520 [01:09<31:11, 3.71s/it] {'loss': 1.514, 'grad_norm': 0.004260937548356311, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:09<31:11, 3.71s/it] 3%|▎ | 17/520 [01:13<30:50, 3.68s/it] {'loss': 1.5968, 'grad_norm': 0.00432186487705712, 'learning_rate': 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23/520 [01:35<30:05, 3.63s/it] {'loss': 1.5191, 'grad_norm': 0.003944003879062514, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:35<30:05, 3.63s/it] 5%|▍ | 24/520 [01:38<30:09, 3.65s/it] {'loss': 1.5034, 'grad_norm': 0.0035137030667676534, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:38<30:09, 3.65s/it] 5%|▍ | 25/520 [01:42<30:21, 3.68s/it] {'loss': 1.5405, 'grad_norm': 0.003840899793754263, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:42<30:21, 3.68s/it] 5%|▌ | 26/520 [01:46<30:31, 3.71s/it] {'loss': 1.5294, 'grad_norm': 0.0031308947615881983, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:46<30:31, 3.71s/it] 5%|▌ | 27/520 [01:50<30:42, 3.74s/it] {'loss': 1.4352, 'grad_norm': 0.0035916411766910186, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:50<30:42, 3.74s/it] 5%|▌ | 28/520 [01:53<30:45, 3.75s/it] {'loss': 1.4193, 'grad_norm': 0.0031319407895938913, 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3.80s/it] 7%|▋ | 34/520 [02:16<30:47, 3.80s/it] {'loss': 1.4241, 'grad_norm': 0.004122850239823112, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:16<30:47, 3.80s/it] 7%|▋ | 35/520 [02:20<30:38, 3.79s/it] {'loss': 1.4538, 'grad_norm': 0.004763380281714584, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:20<30:38, 3.79s/it] 7%|▋ | 36/520 [02:24<30:32, 3.79s/it] {'loss': 1.5598, 'grad_norm': 0.0032482735805940684, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:24<30:32, 3.79s/it] 7%|▋ | 37/520 [02:27<30:14, 3.76s/it] {'loss': 1.632, 'grad_norm': 0.005486005375536932, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:27<30:14, 3.76s/it] 7%|▋ | 38/520 [02:31<29:47, 3.71s/it] {'loss': 1.6339, 'grad_norm': 0.004483297579647113, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:31<29:47, 3.71s/it] 8%|▊ | 39/520 [02:35<29:27, 3.67s/it] {'loss': 1.4584, 'grad_norm': 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0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:45<12:34, 3.73s/it] 61%|██████▏ | 319/520 [19:49<12:50, 3.83s/it] {'loss': 1.1856, 'grad_norm': 0.002075419098047798, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:49<12:50, 3.83s/it] 62%|██████▏ | 320/520 [19:52<12:33, 3.77s/it] {'loss': 1.1306, 'grad_norm': 0.00211964528304433, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:52<12:33, 3.77s/it] 62%|██████▏ | 321/520 [19:56<12:22, 3.73s/it] {'loss': 1.3263, 'grad_norm': 0.002156291362892726, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [19:56<12:22, 3.73s/it] 62%|██████▏ | 322/520 [19:59<12:12, 3.70s/it] {'loss': 1.1876, 'grad_norm': 0.002033776767803461, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [19:59<12:12, 3.70s/it] 62%|██████▏ | 323/520 [20:03<12:04, 3.68s/it] {'loss': 1.2669, 'grad_norm': 0.0022058274467354096, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:03<12:04, 3.68s/it] 62%|██████▏ | 324/520 [20:07<11:57, 3.66s/it] {'loss': 1.2614, 'grad_norm': 0.002268263260796566, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:07<11:57, 3.66s/it] 62%|██████▎ | 325/520 [20:10<11:52, 3.65s/it] {'loss': 1.2736, 'grad_norm': 0.0021749810635541527, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:10<11:52, 3.65s/it] 63%|██████▎ | 326/520 [20:14<11:46, 3.64s/it] {'loss': 1.2556, 'grad_norm': 0.0020241902403820527, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:14<11:46, 3.64s/it] 63%|██████▎ | 327/520 [20:18<11:40, 3.63s/it] {'loss': 1.3366, 'grad_norm': 0.002282784522213298, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:18<11:40, 3.63s/it] 63%|██████▎ | 328/520 [20:21<11:35, 3.62s/it] {'loss': 1.319, 'grad_norm': 0.0021500322305545976, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:21<11:35, 3.62s/it] 63%|██████▎ | 329/520 [20:25<11:31, 3.62s/it] {'loss': 1.1726, 'grad_norm': 0.0017564754062394637, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:25<11:31, 3.62s/it] 63%|██████▎ | 330/520 [20:28<11:30, 3.63s/it] {'loss': 1.2507, 'grad_norm': 0.001812466717575198, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:28<11:30, 3.63s/it] 64%|██████▎ | 331/520 [20:32<11:26, 3.63s/it] {'loss': 1.2087, 'grad_norm': 0.0019019563174958278, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:32<11:26, 3.63s/it] 64%|██████▍ | 332/520 [20:36<11:22, 3.63s/it] {'loss': 1.3454, 'grad_norm': 0.001966983357401263, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:36<11:22, 3.63s/it] 64%|██████▍ | 333/520 [20:39<11:17, 3.62s/it] {'loss': 1.3735, 'grad_norm': 0.0021282169673536573, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:39<11:17, 3.62s/it] 64%|██████▍ | 334/520 [20:43<11:14, 3.63s/it] {'loss': 1.2573, 'grad_norm': 0.0024002026259134987, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:43<11:14, 3.63s/it] 64%|██████▍ | 335/520 [20:47<11:10, 3.63s/it] {'loss': 1.2537, 'grad_norm': 0.0018272023041679094, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:47<11:10, 3.63s/it] 65%|██████▍ | 336/520 [20:50<11:06, 3.62s/it] {'loss': 1.1428, 'grad_norm': 0.002192567232808588, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:50<11:06, 3.62s/it] 65%|██████▍ | 337/520 [20:54<11:02, 3.62s/it] {'loss': 1.1359, 'grad_norm': 0.0020063677609322202, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:54<11:02, 3.62s/it] 65%|██████▌ | 338/520 [20:57<10:59, 3.62s/it] {'loss': 1.2667, 'grad_norm': 0.0020524465195644044, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [20:57<10:59, 3.62s/it] 65%|██████▌ | 339/520 [21:01<10:56, 3.63s/it] {'loss': 1.2082, 'grad_norm': 0.0020006593279641672, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:01<10:56, 3.63s/it] 65%|██████▌ | 340/520 [21:05<10:53, 3.63s/it] {'loss': 1.2014, 'grad_norm': 0.00202507703900229, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:05<10:53, 3.63s/it] 66%|██████▌ | 341/520 [21:08<10:54, 3.66s/it] {'loss': 1.2183, 'grad_norm': 0.0021106083267615535, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:08<10:54, 3.66s/it] 66%|██████▌ | 342/520 [21:12<10:50, 3.65s/it] {'loss': 1.3153, 'grad_norm': 0.0023673979976475946, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:12<10:50, 3.65s/it] 66%|██████▌ | 343/520 [21:16<10:46, 3.65s/it] {'loss': 1.2735, 'grad_norm': 0.002009433980187689, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:16<10:46, 3.65s/it] 66%|██████▌ | 344/520 [21:19<10:43, 3.66s/it] {'loss': 1.1736, 'grad_norm': 0.00212572117230679, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:19<10:43, 3.66s/it] 66%|██████▋ | 345/520 [21:23<10:40, 3.66s/it] {'loss': 1.2905, 'grad_norm': 0.0023176133962373455, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:23<10:40, 3.66s/it] 67%|██████▋ | 346/520 [21:27<10:35, 3.65s/it] {'loss': 1.2663, 'grad_norm': 0.0018671903417676612, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:27<10:35, 3.65s/it] 67%|██████▋ | 347/520 [21:30<10:31, 3.65s/it] {'loss': 1.1879, 'grad_norm': 0.0018585948854783886, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:30<10:31, 3.65s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:34<10:27, 3.65s/it] {'loss': 1.1476, 'grad_norm': 0.0024931707134771205, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:34<10:27, 3.65s/it] 67%|██████▋ | 349/520 [21:38<10:25, 3.66s/it] {'loss': 1.1934, 'grad_norm': 0.0021639970058213256, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:38<10:25, 3.66s/it] 67%|██████▋ | 350/520 [21:41<10:20, 3.65s/it] {'loss': 1.2304, 'grad_norm': 0.002103635963801817, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:41<10:20, 3.65s/it] 68%|██████▊ | 351/520 [21:45<10:18, 3.66s/it] {'loss': 1.1375, 'grad_norm': 0.0019135474463311908, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:45<10:18, 3.66s/it] 68%|██████▊ | 352/520 [21:49<10:14, 3.66s/it] {'loss': 1.2589, 'grad_norm': 0.0019108908593555636, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:49<10:14, 3.66s/it] 68%|██████▊ | 353/520 [21:52<10:12, 3.67s/it] {'loss': 1.2179, 'grad_norm': 0.0021626645690339605, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:52<10:12, 3.67s/it] 68%|██████▊ | 354/520 [21:56<10:07, 3.66s/it] {'loss': 1.3525, 'grad_norm': 0.0019869669913759715, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:56<10:07, 3.66s/it] 68%|██████▊ | 355/520 [22:00<10:04, 3.66s/it] {'loss': 1.1996, 'grad_norm': 0.002041692940576882, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:00<10:04, 3.66s/it] 68%|██████▊ | 356/520 [22:03<10:01, 3.67s/it] {'loss': 1.201, 'grad_norm': 0.0020178325604843796, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:03<10:01, 3.67s/it] 69%|██████▊ | 357/520 [22:07<09:57, 3.66s/it] {'loss': 1.2246, 'grad_norm': 0.0018594324975117484, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:07<09:57, 3.66s/it] 69%|██████▉ | 358/520 [22:11<09:53, 3.66s/it] {'loss': 1.1533, 'grad_norm': 0.001915923864400107, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:11<09:53, 3.66s/it] 69%|██████▉ | 359/520 [22:14<09:48, 3.66s/it] {'loss': 1.285, 'grad_norm': 0.0021384697743394507, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:14<09:48, 3.66s/it] 69%|██████▉ | 360/520 [22:18<09:44, 3.65s/it] {'loss': 1.3159, 'grad_norm': 0.0031899965138430047, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:18<09:44, 3.65s/it] 69%|██████▉ | 361/520 [22:22<09:41, 3.66s/it] {'loss': 1.297, 'grad_norm': 0.0018836984545226897, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:22<09:41, 3.66s/it] 70%|██████▉ | 362/520 [22:25<09:36, 3.65s/it] {'loss': 1.2206, 'grad_norm': 0.0021390729373984676, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:25<09:36, 3.65s/it] 70%|██████▉ | 363/520 [22:29<09:32, 3.65s/it] {'loss': 1.2352, 'grad_norm': 0.001956975488380928, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:29<09:32, 3.65s/it] 70%|███████ | 364/520 [22:33<09:30, 3.66s/it] {'loss': 1.3076, 'grad_norm': 0.001970593065476959, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:33<09:30, 3.66s/it] 70%|███████ | 365/520 [22:36<09:26, 3.66s/it] {'loss': 1.3038, 'grad_norm': 0.0020755555385245346, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:36<09:26, 3.66s/it] 70%|███████ | 366/520 [22:40<09:30, 3.71s/it] {'loss': 1.2532, 'grad_norm': 0.0019361355946813552, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:40<09:30, 3.71s/it] 71%|███████ | 367/520 [22:44<09:32, 3.74s/it] {'loss': 1.2468, 'grad_norm': 0.0019408148807448843, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:44<09:32, 3.74s/it] 71%|███████ | 368/520 [22:48<09:32, 3.77s/it] {'loss': 1.109, 'grad_norm': 0.0021854411972903006, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:48<09:32, 3.77s/it] 71%|███████ | 369/520 [22:51<09:31, 3.78s/it] {'loss': 1.2634, 'grad_norm': 0.0018181094713437336, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:51<09:31, 3.78s/it] 71%|███████ | 370/520 [22:55<09:27, 3.79s/it] {'loss': 1.1681, 'grad_norm': 0.0017964926931982575, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:55<09:27, 3.79s/it] 71%|███████▏ | 371/520 [22:59<09:25, 3.79s/it] {'loss': 1.1661, 'grad_norm': 0.002082473372124311, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [22:59<09:25, 3.79s/it] 72%|███████▏ | 372/520 [23:03<09:22, 3.80s/it] {'loss': 1.3588, 'grad_norm': 0.0018821730927485987, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:03<09:22, 3.80s/it] 72%|███████▏ | 373/520 [23:07<09:19, 3.81s/it] {'loss': 1.2315, 'grad_norm': 0.0020748073134927046, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:07<09:19, 3.81s/it] 72%|███████▏ | 374/520 [23:11<09:15, 3.80s/it] {'loss': 1.2457, 'grad_norm': 0.0019229034192709393, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:11<09:15, 3.80s/it] 72%|███████▏ | 375/520 [23:14<09:12, 3.81s/it] {'loss': 1.1629, 'grad_norm': 0.002032120137789063, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:14<09:12, 3.81s/it] 72%|███████▏ | 376/520 [23:18<09:09, 3.82s/it] {'loss': 1.2849, 'grad_norm': 0.0021014377454096184, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:18<09:09, 3.82s/it] 72%|███████▎ | 377/520 [23:22<09:05, 3.82s/it] {'loss': 1.2149, 'grad_norm': 0.0020806442661567286, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:22<09:05, 3.82s/it] 73%|███████▎ | 378/520 [23:26<09:00, 3.81s/it] {'loss': 1.2699, 'grad_norm': 0.001928125127404237, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:26<09:00, 3.81s/it] 73%|███████▎ | 379/520 [23:30<08:56, 3.80s/it] {'loss': 1.2471, 'grad_norm': 0.0018638166544617668, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:30<08:56, 3.80s/it] 73%|███████▎ | 380/520 [23:33<08:52, 3.80s/it] {'loss': 1.3481, 'grad_norm': 0.0036376637495252172, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:33<08:52, 3.80s/it] 73%|███████▎ | 381/520 [23:37<08:47, 3.80s/it] {'loss': 1.2472, 'grad_norm': 0.0019276162075155276, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:37<08:47, 3.80s/it] 73%|███████▎ | 382/520 [23:41<08:41, 3.78s/it] {'loss': 1.2825, 'grad_norm': 0.0021173401810871736, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:41<08:41, 3.78s/it] 74%|███████▎ | 383/520 [23:45<08:31, 3.73s/it] {'loss': 1.0881, 'grad_norm': 0.002073892012523576, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:45<08:31, 3.73s/it] 74%|███████▍ | 384/520 [23:48<08:23, 3.70s/it] {'loss': 1.3604, 'grad_norm': 0.002153208958004034, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:48<08:23, 3.70s/it] 74%|███████▍ | 385/520 [23:52<08:16, 3.68s/it] {'loss': 1.2272, 'grad_norm': 0.0018317644052357508, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:52<08:16, 3.68s/it] 74%|███████▍ | 386/520 [23:55<08:09, 3.66s/it] {'loss': 1.176, 'grad_norm': 0.0017182740993321641, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:55<08:09, 3.66s/it] 74%|███████▍ | 387/520 [23:59<08:05, 3.65s/it] {'loss': 1.3581, 'grad_norm': 0.0019810839132662367, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [23:59<08:05, 3.65s/it] 75%|███████▍ | 388/520 [24:03<08:00, 3.64s/it] {'loss': 1.1222, 'grad_norm': 0.0018127569815761736, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:03<08:00, 3.64s/it] 75%|███████▍ | 389/520 [24:06<07:57, 3.64s/it] {'loss': 1.182, 'grad_norm': 0.0022345238980371767, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:06<07:57, 3.64s/it] 75%|███████▌ | 390/520 [24:10<07:53, 3.64s/it] {'loss': 1.2425, 'grad_norm': 0.0018009343379351277, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:10<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:14<07:50, 3.65s/it] {'loss': 1.328, 'grad_norm': 0.0020477042369025774, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:14<07:50, 3.65s/it] 75%|███████▌ | 392/520 [24:17<07:46, 3.65s/it] {'loss': 1.1319, 'grad_norm': 0.0018453399270181626, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:17<07:46, 3.65s/it] 76%|███████▌ | 393/520 [24:21<07:41, 3.64s/it] {'loss': 1.1722, 'grad_norm': 0.001764459854401963, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:21<07:41, 3.64s/it] 76%|███████▌ | 394/520 [24:24<07:38, 3.64s/it] {'loss': 1.1954, 'grad_norm': 0.002130622992896836, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:25<07:38, 3.64s/it] 76%|███████▌ | 395/520 [24:28<07:35, 3.64s/it] {'loss': 1.1602, 'grad_norm': 0.002104064054443008, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:28<07:35, 3.64s/it] 76%|███████▌ | 396/520 [24:32<07:31, 3.64s/it] {'loss': 1.2473, 'grad_norm': 0.0020437173662099096, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:32<07:31, 3.64s/it] 76%|███████▋ | 397/520 [24:35<07:27, 3.64s/it] {'loss': 1.231, 'grad_norm': 0.0018336123178139554, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:35<07:27, 3.64s/it] 77%|███████▋ | 398/520 [24:39<07:24, 3.64s/it] {'loss': 1.224, 'grad_norm': 0.0019796333490184666, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:39<07:24, 3.64s/it] 77%|███████▋ | 399/520 [24:43<07:21, 3.65s/it] {'loss': 1.2094, 'grad_norm': 0.0018719757812490588, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:43<07:21, 3.65s/it] 77%|███████▋ | 400/520 [24:46<07:18, 3.66s/it] {'loss': 1.2593, 'grad_norm': 0.0020149668517393895, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:46<07:18, 3.66s/it] 77%|███████▋ | 401/520 [24:50<07:17, 3.67s/it] {'loss': 1.0555, 'grad_norm': 0.0020868516837255512, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:50<07:17, 3.67s/it] 77%|███████▋ | 402/520 [24:54<07:13, 3.67s/it] {'loss': 1.173, 'grad_norm': 0.002070291879165328, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:54<07:13, 3.67s/it] 78%|███████▊ | 403/520 [24:57<07:08, 3.66s/it] {'loss': 1.2038, 'grad_norm': 0.0021795551715782764, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [24:57<07:08, 3.66s/it] 78%|███████▊ | 404/520 [25:01<07:03, 3.65s/it] {'loss': 1.111, 'grad_norm': 0.002346417308006902, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:01<07:03, 3.65s/it] 78%|███████▊ | 405/520 [25:05<06:59, 3.65s/it] {'loss': 1.2177, 'grad_norm': 0.0018932764502143174, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:05<06:59, 3.65s/it] 78%|███████▊ | 406/520 [25:08<06:56, 3.66s/it] {'loss': 1.1573, 'grad_norm': 0.0023523221067772745, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:08<06:56, 3.66s/it] 78%|███████▊ | 407/520 [25:12<06:52, 3.65s/it] {'loss': 1.2948, 'grad_norm': 0.001976896640520199, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:12<06:52, 3.65s/it] 78%|███████▊ | 408/520 [25:16<06:47, 3.64s/it] {'loss': 1.1894, 'grad_norm': 0.002252925093138779, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:16<06:47, 3.64s/it] 79%|███████▊ | 409/520 [25:19<06:43, 3.63s/it] {'loss': 1.3172, 'grad_norm': 0.002189029499160059, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:19<06:43, 3.63s/it] 79%|███████▉ | 410/520 [25:23<06:39, 3.63s/it] {'loss': 1.0355, 'grad_norm': 0.0019322957249026094, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:23<06:39, 3.63s/it] 79%|███████▉ | 411/520 [25:27<06:35, 3.63s/it] {'loss': 1.2926, 'grad_norm': 0.002261249327236201, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:27<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:30<06:32, 3.64s/it] {'loss': 1.2041, 'grad_norm': 0.00195963912668719, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:30<06:32, 3.64s/it] 79%|███████▉ | 413/520 [25:34<06:28, 3.63s/it] {'loss': 1.2472, 'grad_norm': 0.0018885623896078086, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:34<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:37<06:25, 3.63s/it] {'loss': 1.043, 'grad_norm': 0.0016963970860832155, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:37<06:25, 3.63s/it] 80%|███████▉ | 415/520 [25:41<06:20, 3.63s/it] {'loss': 1.1775, 'grad_norm': 0.0018867905143791783, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:41<06:20, 3.63s/it] 80%|████████ | 416/520 [25:45<06:17, 3.63s/it] {'loss': 1.0978, 'grad_norm': 0.0021818313775629886, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:45<06:17, 3.63s/it] 80%|████████ | 417/520 [25:48<06:16, 3.65s/it] {'loss': 1.2649, 'grad_norm': 0.0021693606080184177, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:48<06:16, 3.65s/it] 80%|████████ | 418/520 [25:52<06:11, 3.64s/it] {'loss': 1.2426, 'grad_norm': 0.0018915665573866981, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:52<06:11, 3.64s/it] 81%|████████ | 419/520 [25:56<06:06, 3.63s/it] {'loss': 1.233, 'grad_norm': 0.0020562540915212797, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:56<06:06, 3.63s/it] 81%|████████ | 420/520 [25:59<06:02, 3.62s/it] {'loss': 1.1211, 'grad_norm': 0.002057564335093003, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [25:59<06:02, 3.62s/it] 81%|████████ | 421/520 [26:03<05:59, 3.63s/it] {'loss': 1.0519, 'grad_norm': 0.002147272540901587, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:03<05:59, 3.63s/it] 81%|████████ | 422/520 [26:06<05:54, 3.62s/it] {'loss': 1.1784, 'grad_norm': 0.002071532822719271, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:06<05:54, 3.62s/it] 81%|████████▏ | 423/520 [26:10<05:51, 3.63s/it] {'loss': 1.1642, 'grad_norm': 0.002353456417319705, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:10<05:51, 3.63s/it] 82%|████████▏ | 424/520 [26:14<05:48, 3.63s/it] {'loss': 1.3273, 'grad_norm': 0.0020792158836347744, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:14<05:48, 3.63s/it] 82%|████████▏ | 425/520 [26:17<05:44, 3.63s/it] {'loss': 1.1698, 'grad_norm': 0.001838773607766553, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:17<05:44, 3.63s/it] 82%|████████▏ | 426/520 [26:21<05:40, 3.62s/it] {'loss': 1.196, 'grad_norm': 0.002601003888928758, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:21<05:40, 3.62s/it] 82%|████████▏ | 427/520 [26:25<05:36, 3.62s/it] {'loss': 1.1056, 'grad_norm': 0.001976621567800985, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:25<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:28<05:33, 3.62s/it] {'loss': 1.0891, 'grad_norm': 0.00207091138655072, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:28<05:33, 3.62s/it] 82%|████████▎ | 429/520 [26:32<05:31, 3.64s/it] {'loss': 1.1823, 'grad_norm': 0.001928908294024534, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:32<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:35<05:26, 3.63s/it] {'loss': 1.1813, 'grad_norm': 0.0017766084683741147, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:35<05:26, 3.63s/it] 83%|████████▎ | 431/520 [26:39<05:23, 3.63s/it] {'loss': 1.2135, 'grad_norm': 0.002097499588792175, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:39<05:23, 3.63s/it] 83%|████████▎ | 432/520 [26:43<05:19, 3.64s/it] {'loss': 1.0953, 'grad_norm': 0.0020539943736622565, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:43<05:19, 3.64s/it] 83%|████████▎ | 433/520 [26:46<05:16, 3.63s/it] {'loss': 1.2275, 'grad_norm': 0.0018974725331020595, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:46<05:16, 3.63s/it] 83%|████████▎ | 434/520 [26:50<05:13, 3.64s/it] {'loss': 0.968, 'grad_norm': 0.0018749682893671846, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:50<05:13, 3.64s/it] 84%|████████▎ | 435/520 [26:54<05:09, 3.64s/it] {'loss': 1.2616, 'grad_norm': 0.0022404094729589572, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:54<05:09, 3.64s/it] 84%|████████▍ | 436/520 [26:57<05:05, 3.64s/it] {'loss': 1.0512, 'grad_norm': 0.0019065532049328479, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:57<05:05, 3.64s/it] 84%|████████▍ | 437/520 [27:01<05:01, 3.63s/it] {'loss': 1.2872, 'grad_norm': 0.0019446988088845291, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:01<05:01, 3.63s/it] 84%|████████▍ | 438/520 [27:05<05:01, 3.68s/it] {'loss': 1.097, 'grad_norm': 0.001918713899490917, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:05<05:01, 3.68s/it] 84%|████████▍ | 439/520 [27:09<05:02, 3.73s/it] {'loss': 1.187, 'grad_norm': 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3.78s/it] {'loss': 1.0434, 'grad_norm': 0.001920661353667399, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [31:37<00:30, 3.78s/it] 99%|█████████▊| 513/520 [31:41<00:26, 3.75s/it] {'loss': 1.251, 'grad_norm': 0.0021884113415299687, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [31:41<00:26, 3.75s/it] 99%|█████████▉| 514/520 [31:44<00:22, 3.72s/it] {'loss': 1.2197, 'grad_norm': 0.0017984688802973841, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 99%|█████████▉| 514/520 [31:44<00:22, 3.72s/it] 99%|█████████▉| 515/520 [31:48<00:18, 3.68s/it] {'loss': 1.2654, 'grad_norm': 0.002214936475225008, 'learning_rate': 4.856389714723575e-05, 'epoch': 0.99} + 99%|█████████▉| 515/520 [31:48<00:18, 3.68s/it] 99%|█████████▉| 516/520 [31:52<00:14, 3.67s/it] {'loss': 1.1618, 'grad_norm': 0.0018756906173991728, 'learning_rate': 3.108179991837545e-05, 'epoch': 0.99} + 99%|█████████▉| 516/520 [31:52<00:14, 3.67s/it] 99%|█████████▉| 517/520 [31:55<00:10, 3.64s/it] {'loss': 1.2763, 'grad_norm': 0.002065489551841633, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [31:55<00:10, 3.64s/it] 100%|█████████▉| 518/520 [31:59<00:07, 3.62s/it] {'loss': 1.1851, 'grad_norm': 0.0020272049693646193, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [31:59<00:07, 3.62s/it] 100%|█████████▉| 519/520 [32:02<00:03, 3.60s/it] {'loss': 1.2162, 'grad_norm': 0.0019323503754027865, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:02<00:03, 3.60s/it] 100%|██████████| 520/520 [32:07<00:00, 3.87s/it] {'loss': 1.2691, 'grad_norm': 0.0020501025628115303, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:07<00:00, 3.87s/it] {'train_runtime': 1927.3454, 'train_samples_per_second': 34.518, 'train_steps_per_second': 0.27, 'train_loss': 1.3284526432936008, 'epoch': 1.0} + 100%|██████████| 520/520 [32:07<00:00, 3.87s/it] 100%|██████████| 520/520 [32:07<00:00, 3.71s/it] +[2025-10-17 16:52:17,090] [INFO] [launch.py:348:main] Process 482275 exits successfully. +[2025-10-17 16:52:17,090] [INFO] [launch.py:348:main] Process 482270 exits successfully. +[2025-10-17 16:52:18,092] [INFO] [launch.py:348:main] Process 482271 exits successfully. +[2025-10-17 16:52:18,092] [INFO] [launch.py:348:main] Process 482274 exits successfully. +[2025-10-17 16:52:18,093] [INFO] [launch.py:348:main] Process 482276 exits successfully. +[2025-10-17 16:52:18,093] [INFO] [launch.py:348:main] Process 482273 exits successfully. +[2025-10-17 16:52:19,095] [INFO] [launch.py:348:main] Process 482272 exits successfully. +[2025-10-17 16:52:22,099] [INFO] [launch.py:348:main] Process 482269 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-3.0_2.3_2e-1_connector-3.0_2.3_2e-1_ablation_20251017_161830.log +Timestamp: 2025-10-17 16:52:24 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation_20251017_165224.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation_20251017_165224.log new file mode 100644 index 0000000000000000000000000000000000000000..77bc5c414353e5924488f212c866386269fa847f --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation_20251017_165224.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation_20251017_165224.log +Timestamp: 2025-10-17 16:52:24 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:52:27,248] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:30,021] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 16:52:30,023] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 5.0 --temperature_attn_text 2.5 --temperature_mlp_text 2.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 5.0 --temperature_attn_vision 2.5 --temperature_mlp_vision 2.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 5.0 --temperature_connector 2.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:52:32,618] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:33,655] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 16:52:33,655] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 16:52:33,656] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 16:52:33,656] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 16:52:33,656] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 16:52:33,656] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 16:52:33,656] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 16:52:33,658] [INFO] [launch.py:253:main] process 504736 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,660] [INFO] [launch.py:253:main] process 504737 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,662] [INFO] [launch.py:253:main] process 504738 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,663] [INFO] [launch.py:253:main] process 504739 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,665] [INFO] [launch.py:253:main] process 504740 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,667] [INFO] [launch.py:253:main] process 504741 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,669] [INFO] [launch.py:253:main] process 504742 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 16:52:33,671] [INFO] [launch.py:253:main] process 504743 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 16:52:40,340] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,549] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,674] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,730] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,730] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,730] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,751] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:40,751] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,752] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 16:52:40,969] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,095] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,155] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,155] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 16:52:41,155] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,157] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,168] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 16:52:41,173] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.5, 'temperature_mlp': 2.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.5, + "temperature_mlp": 2.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:504736:504736 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504743:506320 [7] NCCL INFO ncclCommInitRank comm 0x5633d51e49e0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504739:506301 [3] NCCL INFO ncclCommInitRank comm 0x5564cd1ef380 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504737:506302 [1] NCCL INFO ncclCommInitRank comm 0x55ad5908de50 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504741:506321 [5] NCCL INFO ncclCommInitRank comm 0x5612e8b09390 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504740:506323 [4] NCCL INFO ncclCommInitRank comm 0x55bf185e0430 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504742:506322 [6] NCCL INFO ncclCommInitRank comm 0x55615cf2fcc0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:504738:506303 [2] NCCL INFO ncclCommInitRank comm 0x560936b9e9c0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x3e12bb89bd02a343 - Init COMPLETE +ywang29-vrdb-test1-worker-0:504736:506300 [0] NCCL INFO ncclCommInitRank comm 0x56505b585460 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x3e12bb89bd02a343 - Init COMPLETE +[2025-10-17 16:53:26,396] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 16:53:28,210] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=5.000000 +Pre-training init connector._connector.0.scores: Mean=5.000005 +Pre-training init connector._connector.2.scores: Mean=4.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 16:53:46,402 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 16:53:46,409 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters 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+language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504736:511235 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504737:511242 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504738:511241 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504739:511240 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504743:511238 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504740:511236 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504742:511239 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:504741:511237 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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'grad_norm': 0.011451666632241798, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:37<58:24, 6.79s/it] 1%| | 5/520 [00:41<48:25, 5.64s/it] {'loss': 1.7747, 'grad_norm': 0.00807318379536869, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:41<48:25, 5.64s/it] 1%| | 6/520 [00:45<42:35, 4.97s/it] {'loss': 1.5444, 'grad_norm': 0.0066506206596596736, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:45<42:35, 4.97s/it] 1%|▏ | 7/520 [00:48<38:43, 4.53s/it] {'loss': 1.564, 'grad_norm': 0.009215174689310492, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:48<38:43, 4.53s/it] 2%|▏ | 8/520 [00:53<38:11, 4.48s/it] {'loss': 1.5743, 'grad_norm': 0.006386140208229975, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:53<38:11, 4.48s/it] 2%|▏ | 9/520 [00:56<36:02, 4.23s/it] {'loss': 1.5829, 'grad_norm': 0.003520486364029562, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:56<36:02, 4.23s/it] 2%|▏ | 10/520 [01:00<34:27, 4.05s/it] {'loss': 1.4154, 'grad_norm': 0.0038318824694698445, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [01:00<34:27, 4.05s/it] 2%|▏ | 11/520 [01:04<33:38, 3.96s/it] {'loss': 1.4935, 'grad_norm': 0.004717086144843032, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [01:04<33:38, 3.96s/it] 2%|▏ | 12/520 [01:07<32:39, 3.86s/it] {'loss': 1.4156, 'grad_norm': 0.003677454777773476, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:07<32:39, 3.86s/it][2025-10-17 16:55:02,772] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:12<33:39, 3.98s/it] {'loss': 1.4254, 'grad_norm': 0.0025536817221569613, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:12<33:39, 3.98s/it] 3%|▎ | 14/520 [01:15<32:38, 3.87s/it] {'loss': 1.4718, 'grad_norm': 0.0030213590848640493, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:15<32:38, 3.87s/it] 3%|▎ | 15/520 [01:19<31:58, 3.80s/it] {'loss': 1.4567, 'grad_norm': 0.00270735803258942, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:19<31:58, 3.80s/it] 3%|▎ | 16/520 [01:22<31:27, 3.75s/it] {'loss': 1.4116, 'grad_norm': 0.002386199003984996, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:22<31:27, 3.75s/it] 3%|▎ | 17/520 [01:26<31:02, 3.70s/it] {'loss': 1.5014, 'grad_norm': 0.0026275233256441394, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:26<31:02, 3.70s/it] 3%|▎ | 18/520 [01:30<30:54, 3.69s/it] {'loss': 1.3521, 'grad_norm': 0.0022593084004074483, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:30<30:54, 3.69s/it] 4%|▎ | 19/520 [01:33<30:43, 3.68s/it] {'loss': 1.4053, 'grad_norm': 0.0019240228663339617, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:33<30:43, 3.68s/it] 4%|▍ | 20/520 [01:37<30:28, 3.66s/it] {'loss': 1.3437, 'grad_norm': 0.0023959709583148956, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:37<30:28, 3.66s/it] 4%|▍ | 21/520 [01:41<30:28, 3.66s/it] {'loss': 1.3977, 'grad_norm': 0.0022026554760227438, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:41<30:28, 3.66s/it] 4%|▍ | 22/520 [01:44<30:20, 3.66s/it] {'loss': 1.4929, 'grad_norm': 0.0020558665672807067, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:44<30:20, 3.66s/it] 4%|▍ 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0.0014786121320126114, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [19:00<13:18, 3.68s/it] 58%|█████▊ | 304/520 [19:03<13:14, 3.68s/it] {'loss': 1.1784, 'grad_norm': 0.0014326976840298444, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:03<13:14, 3.68s/it] 59%|█████▊ | 305/520 [19:07<13:08, 3.67s/it] {'loss': 1.3028, 'grad_norm': 0.0015312114721872676, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:07<13:08, 3.67s/it] 59%|█████▉ | 306/520 [19:11<13:01, 3.65s/it] {'loss': 1.2497, 'grad_norm': 0.001280899033155007, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:11<13:01, 3.65s/it] 59%|█████▉ | 307/520 [19:15<13:21, 3.76s/it] {'loss': 1.1838, 'grad_norm': 0.0012102529086319834, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:15<13:21, 3.76s/it] 59%|█████▉ | 308/520 [19:18<13:09, 3.72s/it] {'loss': 1.305, 'grad_norm': 0.0014316134250205764, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:18<13:09, 3.72s/it] 59%|█████▉ | 309/520 [19:22<13:00, 3.70s/it] {'loss': 1.1917, 'grad_norm': 0.0012402934213321024, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:22<13:00, 3.70s/it] 60%|█████▉ | 310/520 [19:26<12:52, 3.68s/it] {'loss': 1.1719, 'grad_norm': 0.0013174414914615539, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:26<12:52, 3.68s/it] 60%|█████▉ | 311/520 [19:29<12:48, 3.67s/it] {'loss': 1.143, 'grad_norm': 0.0013317015207372966, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:29<12:48, 3.67s/it] 60%|██████ | 312/520 [19:33<12:43, 3.67s/it] {'loss': 1.134, 'grad_norm': 0.001433361377917668, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:33<12:43, 3.67s/it] 60%|██████ | 313/520 [19:37<12:37, 3.66s/it] {'loss': 1.1218, 'grad_norm': 0.0011999636231072896, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:37<12:37, 3.66s/it] 60%|██████ | 314/520 [19:41<12:51, 3.75s/it] {'loss': 1.1598, 'grad_norm': 0.0011634285459403658, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:41<12:51, 3.75s/it] 61%|██████ | 315/520 [19:44<12:40, 3.71s/it] {'loss': 1.2257, 'grad_norm': 0.0017174165989378065, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:44<12:40, 3.71s/it] 61%|██████ | 316/520 [19:48<13:01, 3.83s/it] {'loss': 1.1383, 'grad_norm': 0.0015118168118694309, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:48<13:01, 3.83s/it] 61%|██████ | 317/520 [19:52<12:46, 3.77s/it] {'loss': 1.1565, 'grad_norm': 0.00117359293106054, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:52<12:46, 3.77s/it] 61%|██████ | 318/520 [19:56<12:35, 3.74s/it] {'loss': 1.2653, 'grad_norm': 0.0014384080110408683, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:56<12:35, 3.74s/it] 61%|██████▏ | 319/520 [20:00<12:52, 3.84s/it] {'loss': 1.1397, 'grad_norm': 0.0012236240360234034, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:00<12:52, 3.84s/it] 62%|██████▏ | 320/520 [20:03<12:40, 3.80s/it] {'loss': 1.0867, 'grad_norm': 0.0013289380212500401, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:03<12:40, 3.80s/it] 62%|██████▏ | 321/520 [20:07<12:29, 3.77s/it] {'loss': 1.284, 'grad_norm': 0.0013878675788472218, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:07<12:29, 3.77s/it] 62%|██████▏ | 322/520 [20:11<12:17, 3.73s/it] {'loss': 1.1246, 'grad_norm': 0.001253319418232441, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:11<12:17, 3.73s/it] 62%|██████▏ | 323/520 [20:14<12:09, 3.70s/it] {'loss': 1.193, 'grad_norm': 0.0012255188350226568, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:14<12:09, 3.70s/it] 62%|██████▏ | 324/520 [20:18<12:03, 3.69s/it] {'loss': 1.2198, 'grad_norm': 0.0012879065864451163, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:18<12:03, 3.69s/it] 62%|██████▎ | 325/520 [20:22<11:57, 3.68s/it] {'loss': 1.2279, 'grad_norm': 0.0013300320565523122, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:22<11:57, 3.68s/it] 63%|██████▎ | 326/520 [20:25<11:51, 3.67s/it] {'loss': 1.2179, 'grad_norm': 0.0013294692048877606, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:25<11:51, 3.67s/it] 63%|██████▎ | 327/520 [20:29<11:47, 3.67s/it] {'loss': 1.2378, 'grad_norm': 0.0013986502217052201, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:29<11:47, 3.67s/it] 63%|██████▎ | 328/520 [20:33<11:44, 3.67s/it] {'loss': 1.2658, 'grad_norm': 0.0013361569842255145, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:33<11:44, 3.67s/it] 63%|██████▎ | 329/520 [20:36<11:40, 3.67s/it] {'loss': 1.1421, 'grad_norm': 0.0011300343432809285, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:36<11:40, 3.67s/it] 63%|██████▎ | 330/520 [20:40<11:34, 3.65s/it] {'loss': 1.2103, 'grad_norm': 0.0011935753614492242, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:40<11:34, 3.65s/it] 64%|██████▎ | 331/520 [20:44<11:31, 3.66s/it] {'loss': 1.1716, 'grad_norm': 0.0012319067480420632, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:44<11:31, 3.66s/it] 64%|██████▍ | 332/520 [20:47<11:26, 3.65s/it] {'loss': 1.2637, 'grad_norm': 0.0012430731179895172, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:47<11:26, 3.65s/it] 64%|██████▍ | 333/520 [20:51<11:19, 3.64s/it] {'loss': 1.3145, 'grad_norm': 0.001366010501304621, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:51<11:19, 3.64s/it] 64%|██████▍ | 334/520 [20:54<11:16, 3.64s/it] {'loss': 1.224, 'grad_norm': 0.001350778116246391, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:54<11:16, 3.64s/it] 64%|██████▍ | 335/520 [20:58<11:14, 3.64s/it] {'loss': 1.2227, 'grad_norm': 0.0011841821809473482, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:58<11:14, 3.64s/it] 65%|██████▍ | 336/520 [21:02<11:10, 3.65s/it] {'loss': 1.1221, 'grad_norm': 0.0014343359041997519, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:02<11:10, 3.65s/it] 65%|██████▍ | 337/520 [21:05<11:07, 3.65s/it] {'loss': 1.1028, 'grad_norm': 0.001194861187960715, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:05<11:07, 3.65s/it] 65%|██████▌ | 338/520 [21:09<11:05, 3.66s/it] {'loss': 1.2233, 'grad_norm': 0.001271962604630388, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:09<11:05, 3.66s/it] 65%|██████▌ | 339/520 [21:13<11:02, 3.66s/it] {'loss': 1.1698, 'grad_norm': 0.0012468178947646227, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:13<11:02, 3.66s/it] 65%|██████▌ | 340/520 [21:16<10:59, 3.66s/it] {'loss': 1.1622, 'grad_norm': 0.0012407399709871132, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:16<10:59, 3.66s/it] 66%|██████▌ | 341/520 [21:20<10:54, 3.66s/it] {'loss': 1.1842, 'grad_norm': 0.0013452634341786537, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:20<10:54, 3.66s/it] 66%|██████▌ | 342/520 [21:24<10:50, 3.66s/it] {'loss': 1.229, 'grad_norm': 0.0015472600189498508, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:24<10:50, 3.66s/it] 66%|██████▌ | 343/520 [21:27<10:47, 3.66s/it] {'loss': 1.1827, 'grad_norm': 0.0011940855122720443, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:27<10:47, 3.66s/it] 66%|██████▌ | 344/520 [21:31<10:41, 3.64s/it] {'loss': 1.1392, 'grad_norm': 0.001219473033151005, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:31<10:41, 3.64s/it] 66%|██████▋ | 345/520 [21:35<10:37, 3.64s/it] {'loss': 1.2545, 'grad_norm': 0.001362721443676363, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:35<10:37, 3.64s/it] 67%|██████▋ | 346/520 [21:38<10:33, 3.64s/it] {'loss': 1.2015, 'grad_norm': 0.0012072837750931784, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:38<10:33, 3.64s/it] 67%|██████▋ | 347/520 [21:42<10:28, 3.63s/it] {'loss': 1.1541, 'grad_norm': 0.001176744719301643, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:42<10:28, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:46<10:24, 3.63s/it] {'loss': 1.1143, 'grad_norm': 0.001480337658593993, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:46<10:24, 3.63s/it] 67%|██████▋ | 349/520 [21:49<10:21, 3.64s/it] {'loss': 1.1519, 'grad_norm': 0.0012789291238749277, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:49<10:21, 3.64s/it] 67%|██████▋ | 350/520 [21:53<10:18, 3.64s/it] {'loss': 1.1963, 'grad_norm': 0.0013614876119475597, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:53<10:18, 3.64s/it] 68%|██████▊ | 351/520 [21:57<10:21, 3.68s/it] {'loss': 1.1024, 'grad_norm': 0.0011921532672514805, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:57<10:21, 3.68s/it] 68%|██████▊ | 352/520 [22:00<10:23, 3.71s/it] {'loss': 1.2279, 'grad_norm': 0.0012478729451259783, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:00<10:23, 3.71s/it] 68%|██████▊ | 353/520 [22:04<10:24, 3.74s/it] {'loss': 1.1533, 'grad_norm': 0.0010818364564297443, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:04<10:24, 3.74s/it] 68%|██████▊ | 354/520 [22:08<10:23, 3.76s/it] {'loss': 1.2724, 'grad_norm': 0.0011718403824046695, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:08<10:23, 3.76s/it] 68%|██████▊ | 355/520 [22:12<10:21, 3.77s/it] {'loss': 1.1647, 'grad_norm': 0.0012645973026673386, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:12<10:21, 3.77s/it] 68%|██████▊ | 356/520 [22:15<10:13, 3.74s/it] {'loss': 1.1665, 'grad_norm': 0.0012866456991039049, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:15<10:13, 3.74s/it] 69%|██████▊ | 357/520 [22:19<10:05, 3.71s/it] {'loss': 1.1958, 'grad_norm': 0.0012499750053959124, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:19<10:05, 3.71s/it] 69%|██████▉ | 358/520 [22:23<10:00, 3.71s/it] {'loss': 1.1283, 'grad_norm': 0.00121919698898103, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:23<10:00, 3.71s/it] 69%|██████▉ | 359/520 [22:26<09:54, 3.69s/it] {'loss': 1.2035, 'grad_norm': 0.0013114429540930928, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:26<09:54, 3.69s/it] 69%|██████▉ | 360/520 [22:30<09:48, 3.68s/it] {'loss': 1.2124, 'grad_norm': 0.0012909442055973465, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:30<09:48, 3.68s/it] 69%|██████▉ | 361/520 [22:34<09:44, 3.68s/it] {'loss': 1.2215, 'grad_norm': 0.0012650914206362512, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:34<09:44, 3.68s/it] 70%|██████▉ | 362/520 [22:37<09:41, 3.68s/it] {'loss': 1.1837, 'grad_norm': 0.0013411288842841802, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:37<09:41, 3.68s/it] 70%|██████▉ | 363/520 [22:41<09:35, 3.66s/it] {'loss': 1.2042, 'grad_norm': 0.0012636333052328907, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:41<09:35, 3.66s/it] 70%|███████ | 364/520 [22:45<09:32, 3.67s/it] {'loss': 1.243, 'grad_norm': 0.0012472194111183713, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:45<09:32, 3.67s/it] 70%|███████ | 365/520 [22:48<09:27, 3.66s/it] {'loss': 1.2612, 'grad_norm': 0.001356725532870829, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:48<09:27, 3.66s/it] 70%|███████ | 366/520 [22:52<09:23, 3.66s/it] {'loss': 1.2193, 'grad_norm': 0.0013054129090105822, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:52<09:23, 3.66s/it] 71%|███████ | 367/520 [22:56<09:20, 3.66s/it] {'loss': 1.2191, 'grad_norm': 0.0012599131029879685, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:56<09:20, 3.66s/it] 71%|███████ | 368/520 [22:59<09:16, 3.66s/it] {'loss': 1.0747, 'grad_norm': 0.0013988787223621517, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:59<09:16, 3.66s/it] 71%|███████ | 369/520 [23:03<09:22, 3.72s/it] {'loss': 1.1958, 'grad_norm': 0.0011099319579686263, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:03<09:22, 3.72s/it] 71%|███████ | 370/520 [23:07<09:25, 3.77s/it] {'loss': 1.134, 'grad_norm': 0.0011803827611921544, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:07<09:25, 3.77s/it] 71%|███████▏ | 371/520 [23:11<09:24, 3.79s/it] {'loss': 1.1312, 'grad_norm': 0.0012898647505242115, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:11<09:24, 3.79s/it] 72%|███████▏ | 372/520 [23:15<09:27, 3.83s/it] {'loss': 1.2718, 'grad_norm': 0.001191761541450812, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:15<09:27, 3.83s/it] 72%|███████▏ | 373/520 [23:19<09:25, 3.85s/it] {'loss': 1.163, 'grad_norm': 0.0013480568094372401, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:19<09:25, 3.85s/it] 72%|███████▏ | 374/520 [23:23<09:24, 3.87s/it] {'loss': 1.218, 'grad_norm': 0.0012548961438968221, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:23<09:24, 3.87s/it] 72%|███████▏ | 375/520 [23:27<09:20, 3.86s/it] {'loss': 1.1364, 'grad_norm': 0.0012738424070783527, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:27<09:20, 3.86s/it] 72%|███████▏ | 376/520 [23:30<09:17, 3.87s/it] {'loss': 1.2487, 'grad_norm': 0.0012388387057141262, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:30<09:17, 3.87s/it] 72%|███████▎ | 377/520 [23:34<09:16, 3.89s/it] {'loss': 1.1814, 'grad_norm': 0.0012711556945835094, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:34<09:16, 3.89s/it] 73%|███████▎ | 378/520 [23:38<09:11, 3.89s/it] {'loss': 1.24, 'grad_norm': 0.001244988783170783, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:38<09:11, 3.89s/it] 73%|███████▎ | 379/520 [23:42<09:05, 3.87s/it] {'loss': 1.2169, 'grad_norm': 0.001185408557622457, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:42<09:05, 3.87s/it] 73%|███████▎ | 380/520 [23:46<09:01, 3.87s/it] {'loss': 1.2474, 'grad_norm': 0.0012936849885851764, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:46<09:01, 3.87s/it] 73%|███████▎ | 381/520 [23:50<08:57, 3.87s/it] {'loss': 1.2228, 'grad_norm': 0.0012358346182916632, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:50<08:57, 3.87s/it] 73%|███████▎ | 382/520 [23:54<08:55, 3.88s/it] {'loss': 1.2106, 'grad_norm': 0.0011900112938234859, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:54<08:55, 3.88s/it] 74%|███████▎ | 383/520 [23:58<08:51, 3.88s/it] {'loss': 1.0577, 'grad_norm': 0.001332144194499047, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:58<08:51, 3.88s/it] 74%|███████▍ | 384/520 [24:01<08:46, 3.87s/it] {'loss': 1.2562, 'grad_norm': 0.0011964206425579505, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:01<08:46, 3.87s/it] 74%|███████▍ | 385/520 [24:05<08:42, 3.87s/it] {'loss': 1.1964, 'grad_norm': 0.001152398719017222, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:05<08:42, 3.87s/it] 74%|███████▍ | 386/520 [24:09<08:37, 3.86s/it] {'loss': 1.1488, 'grad_norm': 0.001082516508514884, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:09<08:37, 3.86s/it] 74%|███████▍ | 387/520 [24:13<08:34, 3.87s/it] {'loss': 1.2719, 'grad_norm': 0.0012183579733313138, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:13<08:34, 3.87s/it] 75%|███████▍ | 388/520 [24:17<08:30, 3.87s/it] {'loss': 1.1027, 'grad_norm': 0.0011880398873029576, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:17<08:30, 3.87s/it] 75%|███████▍ | 389/520 [24:21<08:26, 3.87s/it] {'loss': 1.1516, 'grad_norm': 0.0013704779403530564, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:21<08:26, 3.87s/it] 75%|███████▌ | 390/520 [24:25<08:23, 3.87s/it] {'loss': 1.2174, 'grad_norm': 0.001180062103849429, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:25<08:23, 3.87s/it] 75%|███████▌ | 391/520 [24:29<08:19, 3.87s/it] {'loss': 1.2914, 'grad_norm': 0.0012799642013618822, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:29<08:19, 3.87s/it] 75%|███████▌ | 392/520 [24:32<08:15, 3.87s/it] {'loss': 1.1061, 'grad_norm': 0.0012386223350210723, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:32<08:15, 3.87s/it] 76%|███████▌ | 393/520 [24:36<08:11, 3.87s/it] {'loss': 1.11, 'grad_norm': 0.0010843891032715288, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:36<08:11, 3.87s/it] 76%|███████▌ | 394/520 [24:40<08:07, 3.87s/it] {'loss': 1.1682, 'grad_norm': 0.0013342412728195583, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:40<08:07, 3.87s/it] 76%|███████▌ | 395/520 [24:44<08:03, 3.87s/it] {'loss': 1.136, 'grad_norm': 0.0013485765636853614, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:44<08:03, 3.87s/it] 76%|███████▌ | 396/520 [24:48<07:59, 3.86s/it] {'loss': 1.2184, 'grad_norm': 0.0013794204866876293, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:48<07:59, 3.86s/it] 76%|███████▋ | 397/520 [24:52<07:56, 3.88s/it] {'loss': 1.1957, 'grad_norm': 0.0011826901133296396, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:52<07:56, 3.88s/it] 77%|███████▋ | 398/520 [24:56<07:51, 3.87s/it] {'loss': 1.1985, 'grad_norm': 0.0012880665258766023, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:56<07:51, 3.87s/it] 77%|███████▋ | 399/520 [25:00<07:48, 3.87s/it] {'loss': 1.1547, 'grad_norm': 0.001206087103423419, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [25:00<07:48, 3.87s/it] 77%|███████▋ | 400/520 [25:03<07:43, 3.86s/it] {'loss': 1.1869, 'grad_norm': 0.0011310327016078136, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:03<07:43, 3.86s/it] 77%|███████▋ | 401/520 [25:07<07:31, 3.79s/it] {'loss': 1.0349, 'grad_norm': 0.001476527459565157, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:07<07:31, 3.79s/it] 77%|███████▋ | 402/520 [25:11<07:21, 3.74s/it] {'loss': 1.1515, 'grad_norm': 0.0012572012005907416, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:11<07:21, 3.74s/it] 78%|███████▊ | 403/520 [25:14<07:13, 3.70s/it] {'loss': 1.1782, 'grad_norm': 0.0013571310202550697, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:14<07:13, 3.70s/it] 78%|███████▊ | 404/520 [25:18<07:06, 3.67s/it] {'loss': 1.0845, 'grad_norm': 0.0014986134828061297, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:18<07:06, 3.67s/it] 78%|███████▊ | 405/520 [25:21<07:01, 3.67s/it] {'loss': 1.1633, 'grad_norm': 0.0012596647263571953, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:21<07:01, 3.67s/it] 78%|███████▊ | 406/520 [25:25<06:56, 3.66s/it] {'loss': 1.082, 'grad_norm': 0.00147488957908633, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:25<06:56, 3.66s/it] 78%|███████▊ | 407/520 [25:29<06:51, 3.64s/it] {'loss': 1.2636, 'grad_norm': 0.001280445083138895, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:29<06:51, 3.64s/it] 78%|███████▊ | 408/520 [25:32<06:46, 3.63s/it] {'loss': 1.1672, 'grad_norm': 0.0013687022934040303, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:32<06:46, 3.63s/it] 79%|███████▊ | 409/520 [25:36<06:42, 3.62s/it] {'loss': 1.2857, 'grad_norm': 0.0013286638345283662, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:36<06:42, 3.62s/it] 79%|███████▉ | 410/520 [25:40<06:38, 3.63s/it] {'loss': 1.0166, 'grad_norm': 0.0012441419944276798, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:40<06:38, 3.63s/it] 79%|███████▉ | 411/520 [25:43<06:35, 3.63s/it] {'loss': 1.2645, 'grad_norm': 0.0014973339657783106, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:43<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:47<06:31, 3.63s/it] {'loss': 1.1749, 'grad_norm': 0.0012553477129147094, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:47<06:31, 3.63s/it] 79%|███████▉ | 413/520 [25:50<06:28, 3.63s/it] {'loss': 1.1765, 'grad_norm': 0.001142076413690565, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:50<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:54<06:26, 3.64s/it] {'loss': 0.9886, 'grad_norm': 0.0010333988977324704, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:54<06:26, 3.64s/it] 80%|███████▉ | 415/520 [25:58<06:23, 3.65s/it] {'loss': 1.1517, 'grad_norm': 0.0011737510049105292, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:58<06:23, 3.65s/it] 80%|████████ | 416/520 [26:01<06:18, 3.64s/it] {'loss': 1.0697, 'grad_norm': 0.0013193255723228429, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:01<06:18, 3.64s/it] 80%|████████ | 417/520 [26:05<06:14, 3.64s/it] {'loss': 1.2303, 'grad_norm': 0.001377182381611162, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:05<06:14, 3.64s/it] 80%|████████ | 418/520 [26:09<06:10, 3.63s/it] {'loss': 1.2179, 'grad_norm': 0.0012672800225297864, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:09<06:10, 3.63s/it] 81%|████████ | 419/520 [26:12<06:06, 3.63s/it] {'loss': 1.2097, 'grad_norm': 0.0013837755826579437, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:12<06:06, 3.63s/it] 81%|████████ | 420/520 [26:16<06:02, 3.63s/it] {'loss': 1.1009, 'grad_norm': 0.0013374089153470698, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:16<06:02, 3.63s/it] 81%|████████ | 421/520 [26:20<05:59, 3.64s/it] {'loss': 1.0383, 'grad_norm': 0.0015454058853056518, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:20<05:59, 3.64s/it] 81%|████████ | 422/520 [26:23<05:56, 3.64s/it] {'loss': 1.1553, 'grad_norm': 0.001300405646975024, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:23<05:56, 3.64s/it] 81%|████████▏ | 423/520 [26:27<05:53, 3.65s/it] {'loss': 1.1373, 'grad_norm': 0.001397725671086003, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:27<05:53, 3.65s/it] 82%|████████▏ | 424/520 [26:31<05:50, 3.65s/it] {'loss': 1.2619, 'grad_norm': 0.0013562533339913404, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:31<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:34<05:47, 3.66s/it] {'loss': 1.152, 'grad_norm': 0.0012455636077348883, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:34<05:47, 3.66s/it] 82%|████████▏ | 426/520 [26:38<05:43, 3.66s/it] {'loss': 1.1668, 'grad_norm': 0.0015247499953243847, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:38<05:43, 3.66s/it] 82%|████████▏ | 427/520 [26:42<05:40, 3.66s/it] {'loss': 1.0837, 'grad_norm': 0.0012086894532972504, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:42<05:40, 3.66s/it] 82%|████████▏ | 428/520 [26:45<05:36, 3.65s/it] {'loss': 1.0648, 'grad_norm': 0.001269252134813673, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:45<05:36, 3.65s/it] 82%|████████▎ | 429/520 [26:49<05:32, 3.66s/it] {'loss': 1.1582, 'grad_norm': 0.001247196725493595, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:49<05:32, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:52<05:28, 3.65s/it] {'loss': 1.1633, 'grad_norm': 0.0011606671661908846, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:52<05:28, 3.65s/it] 83%|████████▎ | 431/520 [26:56<05:25, 3.65s/it] {'loss': 1.1455, 'grad_norm': 0.0013446966648033652, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:56<05:25, 3.65s/it] 83%|████████▎ | 432/520 [27:00<05:22, 3.66s/it] {'loss': 1.0711, 'grad_norm': 0.0013005656291900356, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:00<05:22, 3.66s/it] 83%|████████▎ | 433/520 [27:03<05:18, 3.66s/it] {'loss': 1.2008, 'grad_norm': 0.0012309400921626322, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:03<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:07<05:14, 3.66s/it] {'loss': 0.9545, 'grad_norm': 0.0012835286555657598, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:07<05:14, 3.66s/it] 84%|████████▎ | 435/520 [27:11<05:10, 3.65s/it] {'loss': 1.2417, 'grad_norm': 0.001402079618181934, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:11<05:10, 3.65s/it] 84%|████████▍ | 436/520 [27:14<05:06, 3.65s/it] {'loss': 1.0429, 'grad_norm': 0.0013393878846421213, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:14<05:06, 3.65s/it] 84%|████████▍ | 437/520 [27:18<05:02, 3.65s/it] {'loss': 1.261, 'grad_norm': 0.0012658086625341252, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:18<05:02, 3.65s/it] 84%|████████▍ | 438/520 [27:22<05:00, 3.67s/it] {'loss': 1.0794, 'grad_norm': 0.001209417298781214, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:22<05:00, 3.67s/it] 84%|████████▍ | 439/520 [27:25<04:56, 3.66s/it] {'loss': 1.13, 'grad_norm': 0.0010457777408523295, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:25<04:56, 3.66s/it] 85%|████████▍ | 440/520 [27:29<04:51, 3.64s/it] {'loss': 1.1115, 'grad_norm': 0.0012338725821002706, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:29<04:51, 3.64s/it] 85%|████████▍ | 441/520 [27:33<04:47, 3.64s/it] {'loss': 1.1435, 'grad_norm': 0.0011922286527391787, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:33<04:47, 3.64s/it] 85%|████████▌ | 442/520 [27:36<04:44, 3.64s/it] {'loss': 1.1777, 'grad_norm': 0.0013838585171290435, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:36<04:44, 3.64s/it] 85%|████████▌ | 443/520 [27:40<04:39, 3.64s/it] {'loss': 1.1924, 'grad_norm': 0.0012362278872899302, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:40<04:39, 3.64s/it] 85%|████████▌ | 444/520 [27:44<04:37, 3.65s/it] {'loss': 1.1552, 'grad_norm': 0.0011543665964175993, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:44<04:37, 3.65s/it] 86%|████████▌ | 445/520 [27:47<04:32, 3.63s/it] {'loss': 1.0862, 'grad_norm': 0.0012149531278004067, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:47<04:32, 3.63s/it] 86%|████████▌ | 446/520 [27:51<04:29, 3.64s/it] {'loss': 1.2238, 'grad_norm': 0.0012189647424306555, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:51<04:29, 3.64s/it] 86%|████████▌ | 447/520 [27:54<04:26, 3.65s/it] {'loss': 1.1652, 'grad_norm': 0.0012797493217623873, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:55<04:26, 3.65s/it] 86%|████████▌ | 448/520 [27:58<04:21, 3.64s/it] {'loss': 1.1549, 'grad_norm': 0.0012882426677738932, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:58<04:21, 3.64s/it] 86%|████████▋ | 449/520 [28:02<04:18, 3.64s/it] {'loss': 1.1792, 'grad_norm': 0.001292709207876252, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:02<04:18, 3.64s/it] 87%|████████▋ | 450/520 [28:06<04:17, 3.68s/it] {'loss': 1.1847, 'grad_norm': 0.0012678061375944458, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:06<04:17, 3.68s/it] 87%|████████▋ | 451/520 [28:09<04:18, 3.75s/it] {'loss': 1.1796, 'grad_norm': 0.0012575216952884138, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:09<04:18, 3.75s/it] 87%|████████▋ | 452/520 [28:13<04:17, 3.79s/it] {'loss': 1.2198, 'grad_norm': 0.0011594638239519907, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:13<04:17, 3.79s/it] 87%|████████▋ | 453/520 [28:17<04:15, 3.82s/it] {'loss': 1.1939, 'grad_norm': 0.0012608653859438152, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:17<04:15, 3.82s/it] 87%|████████▋ | 454/520 [28:21<04:13, 3.84s/it] {'loss': 1.0917, 'grad_norm': 0.0013085497209814794, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:21<04:13, 3.84s/it] 88%|████████▊ | 455/520 [28:25<04:10, 3.86s/it] {'loss': 1.2291, 'grad_norm': 0.0012333459388529327, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:25<04:10, 3.86s/it] 88%|████████▊ | 456/520 [28:29<04:08, 3.88s/it] {'loss': 1.154, 'grad_norm': 0.0012666140676420921, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:29<04:08, 3.88s/it] 88%|████████▊ | 457/520 [28:33<04:04, 3.87s/it] {'loss': 1.1065, 'grad_norm': 0.0011026220752982123, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:33<04:04, 3.87s/it] 88%|████████▊ | 458/520 [28:37<04:00, 3.87s/it] {'loss': 1.2849, 'grad_norm': 0.0013415566848629602, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:37<04:00, 3.87s/it] 88%|████████▊ | 459/520 [28:41<03:56, 3.88s/it] {'loss': 1.2153, 'grad_norm': 0.0013588567396126611, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:41<03:56, 3.88s/it] 88%|████████▊ | 460/520 [28:44<03:52, 3.88s/it] {'loss': 1.1039, 'grad_norm': 0.0012478549714065655, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:44<03:52, 3.88s/it] 89%|████████▊ | 461/520 [28:48<03:48, 3.88s/it] {'loss': 1.1886, 'grad_norm': 0.0009813974304181395, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:48<03:48, 3.88s/it] 89%|████████▉ | 462/520 [28:52<03:45, 3.88s/it] {'loss': 1.2655, 'grad_norm': 0.0011863455809292278, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:52<03:45, 3.88s/it] 89%|████████▉ | 463/520 [28:56<03:40, 3.88s/it] {'loss': 1.0605, 'grad_norm': 0.0012843517157601107, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:56<03:40, 3.88s/it] 89%|████████▉ | 464/520 [29:00<03:35, 3.85s/it] {'loss': 1.1999, 'grad_norm': 0.0013255154821774843, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [29:00<03:35, 3.85s/it] 89%|████████▉ | 465/520 [29:03<03:27, 3.78s/it] {'loss': 1.3028, 'grad_norm': 0.001345137419566809, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [29:03<03:27, 3.78s/it] 90%|████████▉ | 466/520 [29:07<03:22, 3.75s/it] {'loss': 1.1887, 'grad_norm': 0.0011964888149614548, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:07<03:22, 3.75s/it] 90%|████████▉ | 467/520 [29:11<03:18, 3.74s/it] {'loss': 1.1566, 'grad_norm': 0.0011311453411764025, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:11<03:18, 3.74s/it] 90%|█████████ | 468/520 [29:15<03:13, 3.72s/it] {'loss': 1.1628, 'grad_norm': 0.0013966632553784844, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:15<03:13, 3.72s/it] 90%|█████████ | 469/520 [29:18<03:08, 3.70s/it] {'loss': 1.2238, 'grad_norm': 0.0013846193490874813, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:18<03:08, 3.70s/it] 90%|█████████ | 470/520 [29:22<03:04, 3.68s/it] {'loss': 1.1038, 'grad_norm': 0.0011515625718008064, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:22<03:04, 3.68s/it] 91%|█████████ | 471/520 [29:26<02:59, 3.67s/it] {'loss': 1.129, 'grad_norm': 0.001305246050628068, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:26<02:59, 3.67s/it] 91%|█████████ | 472/520 [29:29<02:56, 3.68s/it] {'loss': 1.0917, 'grad_norm': 0.001311236936007519, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:29<02:56, 3.68s/it] 91%|█████████ | 473/520 [29:33<02:52, 3.66s/it] {'loss': 1.1598, 'grad_norm': 0.001287137189455379, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:33<02:52, 3.66s/it] 91%|█████████ | 474/520 [29:36<02:48, 3.66s/it] {'loss': 1.191, 'grad_norm': 0.001168587574559364, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:36<02:48, 3.66s/it] 91%|█████████▏| 475/520 [29:40<02:44, 3.66s/it] {'loss': 1.1123, 'grad_norm': 0.0011882533202821706, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:40<02:44, 3.66s/it] 92%|█████████▏| 476/520 [29:44<02:41, 3.67s/it] {'loss': 1.1479, 'grad_norm': 0.00129931273385208, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:44<02:41, 3.67s/it] 92%|█████████▏| 477/520 [29:48<02:39, 3.71s/it] {'loss': 1.1375, 'grad_norm': 0.0013605470052738965, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:48<02:39, 3.71s/it] 92%|█████████▏| 478/520 [29:51<02:36, 3.73s/it] {'loss': 1.0899, 'grad_norm': 0.0012905497277274354, 'learning_rate': 0.00340741737109318, 'epoch': 0.92} + 92%|█████████▏| 478/520 [29:51<02:36, 3.73s/it] 92%|█████████▏| 479/520 [29:55<02:33, 3.75s/it] {'loss': 1.1573, 'grad_norm': 0.0013551847162576272, 'learning_rate': 0.003247964765680389, 'epoch': 0.92} + 92%|█████████▏| 479/520 [29:55<02:33, 3.75s/it] 92%|█████████▏| 480/520 [29:59<02:30, 3.76s/it] {'loss': 1.1754, 'grad_norm': 0.0011250582186734775, 'learning_rate': 0.0030922713770922153, 'epoch': 0.92} + 92%|█████████▏| 480/520 [29:59<02:30, 3.76s/it] 92%|█████████▎| 481/520 [30:03<02:27, 3.79s/it] {'loss': 1.1662, 'grad_norm': 0.0011589672030818475, 'learning_rate': 0.0029403432546609046, 'epoch': 0.93} + 92%|█████████▎| 481/520 [30:03<02:27, 3.79s/it] 93%|█████████▎| 482/520 [30:07<02:24, 3.79s/it] {'loss': 1.1892, 'grad_norm': 0.0013537740460344426, 'learning_rate': 0.0027921863014225504, 'epoch': 0.93} + 93%|█████████▎| 482/520 [30:07<02:24, 3.79s/it] 93%|█████████▎| 483/520 [30:10<02:20, 3.80s/it] {'loss': 1.1598, 'grad_norm': 0.0013536460073438874, 'learning_rate': 0.002647806273887665, 'epoch': 0.93} + 93%|█████████▎| 483/520 [30:10<02:20, 3.80s/it] 93%|█████████▎| 484/520 [30:14<02:16, 3.80s/it] {'loss': 1.1641, 'grad_norm': 0.0013014032269387174, 'learning_rate': 0.0025072087818176383, 'epoch': 0.93} + 93%|█████████▎| 484/520 [30:14<02:16, 3.80s/it] 93%|█████████▎| 485/520 [30:18<02:12, 3.79s/it] {'loss': 1.12, 'grad_norm': 0.0012341159686368693, 'learning_rate': 0.002370399288006664, 'epoch': 0.93} + 93%|█████████▎| 485/520 [30:18<02:12, 3.79s/it] 93%|█████████▎| 486/520 [30:22<02:07, 3.76s/it] {'loss': 1.2408, 'grad_norm': 0.0013309733611112395, 'learning_rate': 0.0022373831080695463, 'epoch': 0.93} + 93%|█████████▎| 486/520 [30:22<02:07, 3.76s/it] 94%|█████████▎| 487/520 [30:25<02:02, 3.72s/it] {'loss': 1.0917, 'grad_norm': 0.0012584411003041085, 'learning_rate': 0.0021081654102351635, 'epoch': 0.94} + 94%|█████████▎| 487/520 [30:25<02:02, 3.72s/it] 94%|█████████▍| 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0.96} + 96%|█████████▌| 497/520 [31:02<01:24, 3.66s/it] 96%|█████████▌| 498/520 [31:05<01:20, 3.65s/it] {'loss': 1.1378, 'grad_norm': 0.0012874685197797598, 'learning_rate': 0.000938800558694719, 'epoch': 0.96} + 96%|█████████▌| 498/520 [31:06<01:20, 3.65s/it] 96%|█████████▌| 499/520 [31:09<01:16, 3.67s/it] {'loss': 1.2565, 'grad_norm': 0.0012524473303602214, 'learning_rate': 0.0008555138626189618, 'epoch': 0.96} + 96%|█████████▌| 499/520 [31:09<01:16, 3.67s/it] 96%|█████████▌| 500/520 [31:13<01:13, 3.66s/it] {'loss': 1.256, 'grad_norm': 0.0014347762461623065, 'learning_rate': 0.0007760793399827937, 'epoch': 0.96} + 96%|█████████▌| 500/520 [31:13<01:13, 3.66s/it] 96%|█████████▋| 501/520 [31:16<01:09, 3.65s/it] {'loss': 1.1654, 'grad_norm': 0.001351611749901659, 'learning_rate': 0.000700500077146038, 'epoch': 0.96} + 96%|█████████▋| 501/520 [31:16<01:09, 3.65s/it] 97%|█████████▋| 502/520 [31:20<01:05, 3.64s/it] {'loss': 1.1767, 'grad_norm': 0.0011590361182995208, 'learning_rate': 0.0006287790106757397, 'epoch': 0.97} + 97%|█████████▋| 502/520 [31:20<01:05, 3.64s/it] 97%|█████████▋| 503/520 [31:24<01:01, 3.63s/it] {'loss': 1.1534, 'grad_norm': 0.0012352985293340606, 'learning_rate': 0.0005609189272320237, 'epoch': 0.97} + 97%|█████████▋| 503/520 [31:24<01:01, 3.63s/it] 97%|█████████▋| 504/520 [31:27<00:58, 3.64s/it] {'loss': 1.1668, 'grad_norm': 0.0014150861677419116, 'learning_rate': 0.000496922463459859, 'epoch': 0.97} + 97%|█████████▋| 504/520 [31:27<00:58, 3.64s/it] 97%|█████████▋| 505/520 [31:31<00:54, 3.64s/it] {'loss': 1.2024, 'grad_norm': 0.0013432081524835944, 'learning_rate': 0.0004367921058866187, 'epoch': 0.97} + 97%|█████████▋| 505/520 [31:31<00:54, 3.64s/it] 97%|█████████▋| 506/520 [31:35<00:51, 3.65s/it] {'loss': 1.127, 'grad_norm': 0.0012768332482810878, 'learning_rate': 0.0003805301908254455, 'epoch': 0.97} + 97%|█████████▋| 506/520 [31:35<00:51, 3.65s/it] 98%|█████████▊| 507/520 [31:38<00:47, 3.65s/it] {'loss': 1.2972, 'grad_norm': 0.0011654356273370265, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:38<00:47, 3.65s/it] 98%|█████████▊| 508/520 [31:42<00:43, 3.65s/it] {'loss': 1.248, 'grad_norm': 0.0012797789249995933, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:42<00:43, 3.65s/it] 98%|█████████▊| 509/520 [31:46<00:40, 3.65s/it] {'loss': 1.2176, 'grad_norm': 0.0012443487815439562, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:46<00:40, 3.65s/it] 98%|█████████▊| 510/520 [31:49<00:36, 3.65s/it] {'loss': 1.1645, 'grad_norm': 0.0012280056341368276, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:49<00:36, 3.65s/it] 98%|█████████▊| 511/520 [31:53<00:32, 3.64s/it] {'loss': 1.1403, 'grad_norm': 0.0011986136750815133, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:53<00:32, 3.64s/it] 98%|█████████▊| 512/520 [31:57<00:29, 3.65s/it] {'loss': 1.0264, 'grad_norm': 0.0013731357958642874, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [31:57<00:29, 3.65s/it] 99%|█████████▊| 513/520 [32:00<00:25, 3.65s/it] {'loss': 1.2257, 'grad_norm': 0.001460906990510156, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [32:00<00:25, 3.65s/it] 99%|█████████▉| 514/520 [32:04<00:21, 3.64s/it] {'loss': 1.1931, 'grad_norm': 0.001156845270738848, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 99%|█████████▉| 514/520 [32:04<00:21, 3.64s/it] 99%|█████████▉| 515/520 [32:07<00:18, 3.63s/it] {'loss': 1.2384, 'grad_norm': 0.0014478806981370182, 'learning_rate': 4.856389714723575e-05, 'epoch': 0.99} + 99%|█████████▉| 515/520 [32:07<00:18, 3.63s/it] 99%|█████████▉| 516/520 [32:11<00:14, 3.64s/it] {'loss': 1.143, 'grad_norm': 0.0012115069081707198, 'learning_rate': 3.108179991837545e-05, 'epoch': 0.99} + 99%|█████████▉| 516/520 [32:11<00:14, 3.64s/it] 99%|█████████▉| 517/520 [32:15<00:10, 3.62s/it] {'loss': 1.189, 'grad_norm': 0.0011765084035838843, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:15<00:10, 3.62s/it] 100%|█████████▉| 518/520 [32:18<00:07, 3.59s/it] {'loss': 1.1601, 'grad_norm': 0.0013515334170266398, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:18<00:07, 3.59s/it] 100%|█████████▉| 519/520 [32:22<00:03, 3.58s/it] {'loss': 1.1613, 'grad_norm': 0.0012199899735401358, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:22<00:03, 3.58s/it] 100%|██████████| 520/520 [32:26<00:00, 3.84s/it] {'loss': 1.1633, 'grad_norm': 0.0012431167574976545, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:26<00:00, 3.84s/it] {'train_runtime': 1946.7049, 'train_samples_per_second': 34.175, 'train_steps_per_second': 0.267, 'train_loss': 1.2545986711978911, 'epoch': 1.0} + 100%|██████████| 520/520 [32:26<00:00, 3.84s/it] 100%|██████████| 520/520 [32:26<00:00, 3.74s/it] +[2025-10-17 17:26:22,809] [INFO] [launch.py:348:main] Process 504739 exits successfully. +[2025-10-17 17:26:22,809] [INFO] [launch.py:348:main] Process 504741 exits successfully. +[2025-10-17 17:26:23,811] [INFO] [launch.py:348:main] Process 504742 exits successfully. +[2025-10-17 17:26:23,812] [INFO] [launch.py:348:main] Process 504737 exits successfully. +[2025-10-17 17:26:23,812] [INFO] [launch.py:348:main] Process 504743 exits successfully. +[2025-10-17 17:26:23,812] [INFO] [launch.py:348:main] Process 504740 exits successfully. +[2025-10-17 17:26:24,814] [INFO] [launch.py:348:main] Process 504738 exits successfully. +[2025-10-17 17:26:27,818] [INFO] [launch.py:348:main] Process 504736 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.5_2e-1_connector-5.0_2.5_2e-1_ablation_20251017_165224.log +Timestamp: 2025-10-17 17:26:30 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation_20251017_172630.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation_20251017_172630.log new file mode 100644 index 0000000000000000000000000000000000000000..f0d8ce7068fbb03e26a4ce8ae2c79faadbd147c6 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation_20251017_172630.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation_20251017_172630.log +Timestamp: 2025-10-17 17:26:30 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 17:26:33,060] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:35,977] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 17:26:35,979] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 5.0 --temperature_attn_text 2.7 --temperature_mlp_text 2.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 5.0 --temperature_attn_vision 2.7 --temperature_mlp_vision 2.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 5.0 --temperature_connector 2.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 17:26:38,528] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:39,555] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 17:26:39,555] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 17:26:39,555] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 17:26:39,555] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 17:26:39,555] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 17:26:39,555] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 17:26:39,555] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 17:26:39,558] [INFO] [launch.py:253:main] process 526470 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,559] [INFO] [launch.py:253:main] process 526471 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,561] [INFO] [launch.py:253:main] process 526472 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,563] [INFO] [launch.py:253:main] process 526473 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,565] [INFO] [launch.py:253:main] process 526474 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,567] [INFO] [launch.py:253:main] process 526475 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,569] [INFO] [launch.py:253:main] process 526476 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 17:26:39,571] [INFO] [launch.py:253:main] process 526477 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 17:26:46,367] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,582] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,582] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,642] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,698] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,699] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,702] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,705] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 17:26:46,782] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:46,985] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:46,985] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:47,045] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:47,100] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:47,100] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:47,100] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 17:26:47,101] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 17:26:47,107] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.7, 'temperature_mlp': 2.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.7, + "temperature_mlp": 2.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:526470:526470 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526474:526474 [4] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526473:526473 [3] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526476:526476 [6] NCCL INFO NET/Plugin: Using internal network plugin. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526475:526475 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:526471:526471 [1] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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[10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 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: 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526470:528076 [0] NCCL INFO ncclCommInitRank comm 0x5651b863d580 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526473:528078 [3] NCCL INFO ncclCommInitRank comm 0x559da2bee730 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526471:528081 [1] NCCL INFO ncclCommInitRank comm 0x558157f1cd00 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526472:528083 [2] NCCL INFO ncclCommInitRank comm 0x55c6fd856770 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:526474:528077 [4] NCCL INFO ncclCommInitRank comm 0x55758aa873a0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526476:528079 [6] NCCL INFO ncclCommInitRank comm 0x55835bec13a0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526477:528082 [7] NCCL INFO ncclCommInitRank comm 0x564581244400 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x993aac787f31ac52 - Init COMPLETE +ywang29-vrdb-test1-worker-0:526475:528080 [5] NCCL INFO ncclCommInitRank comm 0x55e8f3955b00 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x993aac787f31ac52 - Init COMPLETE +[2025-10-17 17:27:31,164] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 17:27:32,903] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=5.000000 +Pre-training init connector._connector.0.scores: Mean=5.000005 +Pre-training init connector._connector.2.scores: Mean=4.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 17:27:50,705 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 17:27:50,712 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters 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+language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:003->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526470:532990 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526477:532992 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526473:532991 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526475:532996 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526471:532994 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526472:532995 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526476:532997 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:526474:532993 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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2/520 [00:26<1:38:28, 11.41s/it] {'loss': 2.3049, 'grad_norm': 0.03001818246948031, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:26<1:38:28, 11.41s/it] 1%| | 3/520 [00:29<1:07:33, 7.84s/it] {'loss': 2.4879, 'grad_norm': 0.03427696384231094, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:29<1:07:33, 7.84s/it] 1%| | 4/520 [00:33<53:04, 6.17s/it] {'loss': 1.83, 'grad_norm': 0.012805394576148479, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:33<53:04, 6.17s/it] 1%| | 5/520 [00:36<45:01, 5.25s/it] {'loss': 1.806, 'grad_norm': 0.0087995058474782, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:36<45:01, 5.25s/it] 1%| | 6/520 [00:40<40:10, 4.69s/it] {'loss': 1.5866, 'grad_norm': 0.00750787375140343, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:40<40:10, 4.69s/it] 1%|▏ | 7/520 [00:44<37:01, 4.33s/it] {'loss': 1.5841, 'grad_norm': 0.010067076386584993, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:44<37:01, 4.33s/it] 2%|▏ | 8/520 [00:48<36:41, 4.30s/it] {'loss': 1.5666, 'grad_norm': 0.006381287526441691, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:48<36:41, 4.30s/it] 2%|▏ | 9/520 [00:52<36:13, 4.25s/it] {'loss': 1.597, 'grad_norm': 0.0036867843856714573, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:52<36:13, 4.25s/it] 2%|▏ | 10/520 [00:56<34:26, 4.05s/it] {'loss': 1.4288, 'grad_norm': 0.003832241225292762, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:56<34:26, 4.05s/it] 2%|▏ | 11/520 [00:59<33:30, 3.95s/it] {'loss': 1.4973, 'grad_norm': 0.004421593513246329, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:59<33:30, 3.95s/it] 2%|▏ | 12/520 [01:03<32:33, 3.85s/it] {'loss': 1.4253, 'grad_norm': 0.0035474008456458624, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:03<32:33, 3.85s/it][2025-10-17 17:29:03,001] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:07<33:33, 3.97s/it] {'loss': 1.4359, 'grad_norm': 0.0026570657276102903, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:07<33:33, 3.97s/it] 3%|▎ | 14/520 [01:11<32:38, 3.87s/it] {'loss': 1.4817, 'grad_norm': 0.002999763902817872, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:11<32:38, 3.87s/it] 3%|▎ | 15/520 [01:14<32:02, 3.81s/it] {'loss': 1.4693, 'grad_norm': 0.0025472242525666493, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:14<32:02, 3.81s/it] 3%|▎ | 16/520 [01:18<31:27, 3.74s/it] {'loss': 1.4278, 'grad_norm': 0.002461761166735753, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:18<31:27, 3.74s/it] 3%|▎ | 17/520 [01:22<31:08, 3.71s/it] {'loss': 1.5179, 'grad_norm': 0.002726204680900163, 'learning_rate': 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0.0012594493906424554, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:39<13:27, 3.64s/it] 57%|█████▊ | 299/520 [18:43<13:24, 3.64s/it] {'loss': 1.2666, 'grad_norm': 0.001249407171210859, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:43<13:24, 3.64s/it] 58%|█████▊ | 300/520 [18:46<13:17, 3.63s/it] {'loss': 1.2968, 'grad_norm': 0.001301192770400207, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:46<13:17, 3.63s/it] 58%|█████▊ | 301/520 [18:50<13:18, 3.65s/it] {'loss': 1.2751, 'grad_norm': 0.0013220075604877914, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:50<13:18, 3.65s/it] 58%|█████▊ | 302/520 [18:54<13:14, 3.64s/it] {'loss': 1.2834, 'grad_norm': 0.001329169329114404, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:54<13:14, 3.64s/it] 58%|█████▊ | 303/520 [18:57<13:10, 3.64s/it] {'loss': 1.2019, 'grad_norm': 0.0015293852697797706, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:57<13:10, 3.64s/it] 58%|█████▊ | 304/520 [19:01<13:08, 3.65s/it] {'loss': 1.1825, 'grad_norm': 0.0014749033181141252, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:01<13:08, 3.65s/it] 59%|█████▊ | 305/520 [19:05<13:03, 3.64s/it] {'loss': 1.3093, 'grad_norm': 0.0015186744822961909, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:05<13:03, 3.64s/it] 59%|█████▉ | 306/520 [19:08<12:59, 3.64s/it] {'loss': 1.2534, 'grad_norm': 0.0013293992212209, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:08<12:59, 3.64s/it] 59%|█████▉ | 307/520 [19:12<13:23, 3.77s/it] {'loss': 1.1866, 'grad_norm': 0.001219301537418619, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:12<13:23, 3.77s/it] 59%|█████▉ | 308/520 [19:16<13:13, 3.74s/it] {'loss': 1.3072, 'grad_norm': 0.001418166698031061, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:16<13:13, 3.74s/it] 59%|█████▉ | 309/520 [19:20<13:04, 3.72s/it] {'loss': 1.1937, 'grad_norm': 0.00124532857608784, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:20<13:04, 3.72s/it] 60%|█████▉ | 310/520 [19:23<13:03, 3.73s/it] {'loss': 1.175, 'grad_norm': 0.0013382513553597687, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:23<13:03, 3.73s/it] 60%|█████▉ | 311/520 [19:27<12:59, 3.73s/it] {'loss': 1.1417, 'grad_norm': 0.0013310479268749816, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:27<12:59, 3.73s/it] 60%|██████ | 312/520 [19:31<12:52, 3.72s/it] {'loss': 1.136, 'grad_norm': 0.001467497426472475, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:31<12:52, 3.72s/it] 60%|██████ | 313/520 [19:34<12:44, 3.70s/it] {'loss': 1.1254, 'grad_norm': 0.0011850992000739715, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:34<12:44, 3.70s/it] 60%|██████ | 314/520 [19:39<13:09, 3.83s/it] {'loss': 1.163, 'grad_norm': 0.001187530008986462, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:39<13:09, 3.83s/it] 61%|██████ | 315/520 [19:42<12:55, 3.78s/it] {'loss': 1.2294, 'grad_norm': 0.0017327847460884124, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:42<12:55, 3.78s/it] 61%|██████ | 316/520 [19:46<13:08, 3.86s/it] {'loss': 1.1407, 'grad_norm': 0.0015085822114550378, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:46<13:08, 3.86s/it] 61%|██████ | 317/520 [19:50<12:55, 3.82s/it] {'loss': 1.1582, 'grad_norm': 0.0012268346831416953, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:50<12:55, 3.82s/it] 61%|██████ | 318/520 [19:54<12:47, 3.80s/it] {'loss': 1.2688, 'grad_norm': 0.0014974926155009918, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:54<12:47, 3.80s/it] 61%|██████▏ | 319/520 [19:58<12:55, 3.86s/it] {'loss': 1.1428, 'grad_norm': 0.0012288731417785445, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:58<12:55, 3.86s/it] 62%|██████▏ | 320/520 [20:01<12:39, 3.80s/it] {'loss': 1.0899, 'grad_norm': 0.0013321655312410184, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:01<12:39, 3.80s/it] 62%|██████▏ | 321/520 [20:05<12:26, 3.75s/it] {'loss': 1.2874, 'grad_norm': 0.0014414722909246594, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:05<12:26, 3.75s/it] 62%|██████▏ | 322/520 [20:09<12:16, 3.72s/it] {'loss': 1.124, 'grad_norm': 0.0012599606965245824, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:09<12:16, 3.72s/it] 62%|██████▏ | 323/520 [20:12<12:07, 3.69s/it] {'loss': 1.1971, 'grad_norm': 0.0012986724530532627, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:12<12:07, 3.69s/it] 62%|██████▏ | 324/520 [20:16<12:02, 3.69s/it] {'loss': 1.2207, 'grad_norm': 0.0013718747787409293, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:16<12:02, 3.69s/it] 62%|██████▎ | 325/520 [20:20<11:56, 3.68s/it] {'loss': 1.2296, 'grad_norm': 0.0013889829577781595, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:20<11:56, 3.68s/it] 63%|██████▎ | 326/520 [20:23<11:51, 3.67s/it] {'loss': 1.22, 'grad_norm': 0.0013448511711997852, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:23<11:51, 3.67s/it] 63%|██████▎ | 327/520 [20:27<11:51, 3.69s/it] {'loss': 1.2431, 'grad_norm': 0.0014127058740719845, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:27<11:51, 3.69s/it] 63%|██████▎ | 328/520 [20:31<11:56, 3.73s/it] {'loss': 1.2691, 'grad_norm': 0.0013575500852027246, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:31<11:56, 3.73s/it] 63%|██████▎ | 329/520 [20:35<12:00, 3.77s/it] {'loss': 1.1457, 'grad_norm': 0.0011622429751270743, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:35<12:00, 3.77s/it] 63%|██████▎ | 330/520 [20:39<12:03, 3.81s/it] {'loss': 1.2131, 'grad_norm': 0.0012170308547230608, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:39<12:03, 3.81s/it] 64%|██████▎ | 331/520 [20:43<12:02, 3.82s/it] {'loss': 1.1749, 'grad_norm': 0.0012680541488952635, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:43<12:02, 3.82s/it] 64%|██████▍ | 332/520 [20:46<12:00, 3.83s/it] {'loss': 1.2668, 'grad_norm': 0.0012704380541580268, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:46<12:00, 3.83s/it] 64%|██████▍ | 333/520 [20:50<11:48, 3.79s/it] {'loss': 1.3199, 'grad_norm': 0.001393708955529655, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:50<11:48, 3.79s/it] 64%|██████▍ | 334/520 [20:54<11:40, 3.76s/it] {'loss': 1.2247, 'grad_norm': 0.0013779541195414198, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:54<11:40, 3.76s/it] 64%|██████▍ | 335/520 [20:57<11:33, 3.75s/it] {'loss': 1.224, 'grad_norm': 0.0012138059502573065, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:57<11:33, 3.75s/it] 65%|██████▍ | 336/520 [21:01<11:24, 3.72s/it] {'loss': 1.1213, 'grad_norm': 0.0014264826228248453, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:01<11:24, 3.72s/it] 65%|██████▍ | 337/520 [21:05<11:16, 3.70s/it] {'loss': 1.1051, 'grad_norm': 0.0012736451045661447, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:05<11:16, 3.70s/it] 65%|██████▌ | 338/520 [21:08<11:09, 3.68s/it] {'loss': 1.2236, 'grad_norm': 0.0013001122786137128, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:08<11:09, 3.68s/it] 65%|██████▌ | 339/520 [21:12<11:04, 3.67s/it] {'loss': 1.174, 'grad_norm': 0.0012744180562418632, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:12<11:04, 3.67s/it] 65%|██████▌ | 340/520 [21:16<11:00, 3.67s/it] {'loss': 1.1653, 'grad_norm': 0.0012722360090097445, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:16<11:00, 3.67s/it] 66%|██████▌ | 341/520 [21:19<10:55, 3.66s/it] {'loss': 1.1858, 'grad_norm': 0.0013542921471622503, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:19<10:55, 3.66s/it] 66%|██████▌ | 342/520 [21:23<10:53, 3.67s/it] {'loss': 1.2347, 'grad_norm': 0.0015983241774065941, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:23<10:53, 3.67s/it] 66%|██████▌ | 343/520 [21:27<10:50, 3.68s/it] {'loss': 1.1858, 'grad_norm': 0.00120459482796768, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:27<10:50, 3.68s/it] 66%|██████▌ | 344/520 [21:30<10:45, 3.67s/it] {'loss': 1.1402, 'grad_norm': 0.0012618834817034615, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:30<10:45, 3.67s/it] 66%|██████▋ | 345/520 [21:34<10:42, 3.67s/it] {'loss': 1.2551, 'grad_norm': 0.001397237608663812, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:34<10:42, 3.67s/it] 67%|██████▋ | 346/520 [21:38<10:38, 3.67s/it] {'loss': 1.2033, 'grad_norm': 0.001229099803532126, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:38<10:38, 3.67s/it] 67%|██████▋ | 347/520 [21:41<10:34, 3.67s/it] {'loss': 1.1577, 'grad_norm': 0.0012065197257569733, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:41<10:34, 3.67s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:45<10:30, 3.66s/it] {'loss': 1.1153, 'grad_norm': 0.0015008221241533217, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:45<10:30, 3.66s/it] 67%|██████▋ | 349/520 [21:49<10:25, 3.66s/it] {'loss': 1.1561, 'grad_norm': 0.001311185269031244, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:49<10:25, 3.66s/it] 67%|██████▋ | 350/520 [21:52<10:23, 3.67s/it] {'loss': 1.1985, 'grad_norm': 0.0014334162088660816, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:52<10:23, 3.67s/it] 68%|██████▊ | 351/520 [21:56<10:22, 3.68s/it] {'loss': 1.1031, 'grad_norm': 0.0012198028086472157, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:56<10:22, 3.68s/it] 68%|██████▊ | 352/520 [22:00<10:23, 3.71s/it] {'loss': 1.2303, 'grad_norm': 0.0012946108604719394, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:00<10:23, 3.71s/it] 68%|██████▊ | 353/520 [22:04<10:25, 3.75s/it] {'loss': 1.1583, 'grad_norm': 0.001127984579258125, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:04<10:25, 3.75s/it] 68%|██████▊ | 354/520 [22:08<10:24, 3.76s/it] {'loss': 1.2758, 'grad_norm': 0.0012080421518256277, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:08<10:24, 3.76s/it] 68%|██████▊ | 355/520 [22:11<10:22, 3.77s/it] {'loss': 1.167, 'grad_norm': 0.0013184721361508005, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:11<10:22, 3.77s/it] 68%|██████▊ | 356/520 [22:15<10:20, 3.79s/it] {'loss': 1.1681, 'grad_norm': 0.0013294706743477603, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:15<10:20, 3.79s/it] 69%|██████▊ | 357/520 [22:19<10:10, 3.75s/it] {'loss': 1.1985, 'grad_norm': 0.0012455293291596808, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:19<10:10, 3.75s/it] 69%|██████▉ | 358/520 [22:22<10:01, 3.71s/it] {'loss': 1.1318, 'grad_norm': 0.001270004391399907, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:22<10:01, 3.71s/it] 69%|██████▉ | 359/520 [22:26<09:53, 3.69s/it] {'loss': 1.2058, 'grad_norm': 0.0013302499811500698, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:26<09:53, 3.69s/it] 69%|██████▉ | 360/520 [22:30<09:45, 3.66s/it] {'loss': 1.2185, 'grad_norm': 0.0013384904495737646, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:30<09:45, 3.66s/it] 69%|██████▉ | 361/520 [22:33<09:40, 3.65s/it] {'loss': 1.2253, 'grad_norm': 0.0012162197214761993, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:33<09:40, 3.65s/it] 70%|██████▉ | 362/520 [22:37<09:35, 3.64s/it] {'loss': 1.1857, 'grad_norm': 0.0013755882304510103, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:37<09:35, 3.64s/it] 70%|██████▉ | 363/520 [22:41<09:32, 3.65s/it] {'loss': 1.2056, 'grad_norm': 0.001275952055389112, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:41<09:32, 3.65s/it] 70%|███████ | 364/520 [22:44<09:28, 3.64s/it] {'loss': 1.249, 'grad_norm': 0.0012631724694779848, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:44<09:28, 3.64s/it] 70%|███████ | 365/520 [22:48<09:22, 3.63s/it] {'loss': 1.2636, 'grad_norm': 0.0013704856594368116, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:48<09:22, 3.63s/it] 70%|███████ | 366/520 [22:51<09:19, 3.63s/it] {'loss': 1.2188, 'grad_norm': 0.0013720375281816237, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:51<09:19, 3.63s/it] 71%|███████ | 367/520 [22:55<09:15, 3.63s/it] {'loss': 1.2202, 'grad_norm': 0.001289168954686113, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:55<09:15, 3.63s/it] 71%|███████ | 368/520 [22:59<09:12, 3.64s/it] {'loss': 1.0744, 'grad_norm': 0.001413337873663634, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:59<09:12, 3.64s/it] 71%|███████ | 369/520 [23:02<09:10, 3.64s/it] {'loss': 1.1997, 'grad_norm': 0.0011292060773129514, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:02<09:10, 3.64s/it] 71%|███████ | 370/520 [23:06<09:04, 3.63s/it] {'loss': 1.1347, 'grad_norm': 0.001204280162547154, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:06<09:04, 3.63s/it] 71%|███████▏ | 371/520 [23:10<09:00, 3.63s/it] {'loss': 1.1316, 'grad_norm': 0.0013220180342153021, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:10<09:00, 3.63s/it] 72%|███████▏ | 372/520 [23:13<09:01, 3.66s/it] {'loss': 1.2766, 'grad_norm': 0.0011785147233596683, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:13<09:01, 3.66s/it] 72%|███████▏ | 373/520 [23:17<08:56, 3.65s/it] {'loss': 1.1676, 'grad_norm': 0.0014152070437093852, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:17<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:21<08:58, 3.69s/it] {'loss': 1.217, 'grad_norm': 0.0012976169750471345, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:21<08:58, 3.69s/it] 72%|███████▏ | 375/520 [23:25<08:59, 3.72s/it] {'loss': 1.1378, 'grad_norm': 0.0012954839115078053, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:25<08:59, 3.72s/it] 72%|███████▏ | 376/520 [23:28<08:51, 3.69s/it] {'loss': 1.253, 'grad_norm': 0.0013069698077398844, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:28<08:51, 3.69s/it] 72%|███████▎ | 377/520 [23:32<08:53, 3.73s/it] {'loss': 1.183, 'grad_norm': 0.0013095330036964677, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:32<08:53, 3.73s/it] 73%|███████▎ | 378/520 [23:36<08:54, 3.76s/it] {'loss': 1.2415, 'grad_norm': 0.0012700749119166782, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:36<08:54, 3.76s/it] 73%|███████▎ | 379/520 [23:40<08:53, 3.78s/it] {'loss': 1.2192, 'grad_norm': 0.0012118888768396082, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:40<08:53, 3.78s/it] 73%|███████▎ | 380/520 [23:43<08:50, 3.79s/it] {'loss': 1.2531, 'grad_norm': 0.001303375305379741, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:43<08:50, 3.79s/it] 73%|███████▎ | 381/520 [23:47<08:48, 3.80s/it] {'loss': 1.2238, 'grad_norm': 0.0012684028491883358, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:47<08:48, 3.80s/it] 73%|███████▎ | 382/520 [23:51<08:44, 3.80s/it] {'loss': 1.2138, 'grad_norm': 0.001261289449543064, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:51<08:44, 3.80s/it] 74%|███████▎ | 383/520 [23:55<08:40, 3.80s/it] {'loss': 1.0559, 'grad_norm': 0.0013663853855089873, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:55<08:40, 3.80s/it] 74%|███████▍ | 384/520 [23:59<08:38, 3.81s/it] {'loss': 1.2603, 'grad_norm': 0.0012032465193942078, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:59<08:38, 3.81s/it] 74%|███████▍ | 385/520 [24:03<08:35, 3.82s/it] {'loss': 1.1966, 'grad_norm': 0.001183003595924905, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:03<08:35, 3.82s/it] 74%|███████▍ | 386/520 [24:06<08:32, 3.82s/it] {'loss': 1.1493, 'grad_norm': 0.0011156878058053757, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:06<08:32, 3.82s/it] 74%|███████▍ | 387/520 [24:10<08:27, 3.82s/it] {'loss': 1.2746, 'grad_norm': 0.0012211063973686187, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:10<08:27, 3.82s/it] 75%|███████▍ | 388/520 [24:14<08:25, 3.83s/it] {'loss': 1.1031, 'grad_norm': 0.0012105252617750186, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:14<08:25, 3.83s/it] 75%|███████▍ | 389/520 [24:18<08:21, 3.83s/it] {'loss': 1.152, 'grad_norm': 0.0014140417709401084, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:18<08:21, 3.83s/it] 75%|███████▌ | 390/520 [24:22<08:17, 3.83s/it] {'loss': 1.2199, 'grad_norm': 0.0012342176255504499, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:22<08:17, 3.83s/it] 75%|███████▌ | 391/520 [24:26<08:14, 3.83s/it] {'loss': 1.2948, 'grad_norm': 0.0013329922324260988, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:26<08:14, 3.83s/it] 75%|███████▌ | 392/520 [24:29<08:11, 3.84s/it] {'loss': 1.1085, 'grad_norm': 0.0012594246110021218, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:29<08:11, 3.84s/it] 76%|███████▌ | 393/520 [24:33<08:05, 3.82s/it] {'loss': 1.1161, 'grad_norm': 0.0011367183054719339, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:33<08:05, 3.82s/it] 76%|███████▌ | 394/520 [24:37<07:53, 3.76s/it] {'loss': 1.1681, 'grad_norm': 0.0013898228392357043, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:37<07:53, 3.76s/it] 76%|███████▌ | 395/520 [24:40<07:44, 3.72s/it] {'loss': 1.1375, 'grad_norm': 0.001384265751058344, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:40<07:44, 3.72s/it] 76%|███████▌ | 396/520 [24:44<07:38, 3.70s/it] {'loss': 1.2202, 'grad_norm': 0.0013999973704374373, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:44<07:38, 3.70s/it] 76%|███████▋ | 397/520 [24:48<07:34, 3.70s/it] {'loss': 1.1974, 'grad_norm': 0.001210650473869728, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:48<07:34, 3.70s/it] 77%|███████▋ | 398/520 [24:51<07:28, 3.68s/it] {'loss': 1.1996, 'grad_norm': 0.0013181096962400802, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:51<07:28, 3.68s/it] 77%|███████▋ | 399/520 [24:55<07:25, 3.69s/it] {'loss': 1.1565, 'grad_norm': 0.0012537813013765657, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:55<07:25, 3.69s/it] 77%|███████▋ | 400/520 [24:59<07:20, 3.67s/it] {'loss': 1.1903, 'grad_norm': 0.0011881377550259807, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:59<07:20, 3.67s/it] 77%|███████▋ | 401/520 [25:02<07:15, 3.66s/it] {'loss': 1.0353, 'grad_norm': 0.0014479211878603922, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:02<07:15, 3.66s/it] 77%|███████▋ | 402/520 [25:06<07:11, 3.65s/it] {'loss': 1.1548, 'grad_norm': 0.0013081114787724009, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:06<07:11, 3.65s/it] 78%|███████▊ | 403/520 [25:10<07:06, 3.64s/it] {'loss': 1.1809, 'grad_norm': 0.0014313922468166527, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:10<07:06, 3.64s/it] 78%|███████▊ | 404/520 [25:13<07:02, 3.64s/it] {'loss': 1.0865, 'grad_norm': 0.001526292131712712, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:13<07:02, 3.64s/it] 78%|███████▊ | 405/520 [25:17<06:58, 3.64s/it] {'loss': 1.1661, 'grad_norm': 0.0012848940458111864, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:17<06:58, 3.64s/it] 78%|███████▊ | 406/520 [25:21<06:54, 3.63s/it] {'loss': 1.0856, 'grad_norm': 0.001486231641764367, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:21<06:54, 3.63s/it] 78%|███████▊ | 407/520 [25:24<06:50, 3.64s/it] {'loss': 1.2644, 'grad_norm': 0.001340893886883938, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:24<06:50, 3.64s/it] 78%|███████▊ | 408/520 [25:28<06:47, 3.64s/it] {'loss': 1.1685, 'grad_norm': 0.0013990305427103262, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:28<06:47, 3.64s/it] 79%|███████▊ | 409/520 [25:31<06:42, 3.63s/it] {'loss': 1.2866, 'grad_norm': 0.0013661105348876683, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:31<06:42, 3.63s/it] 79%|███████▉ | 410/520 [25:35<06:38, 3.63s/it] {'loss': 1.0176, 'grad_norm': 0.0012933154880170783, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:35<06:38, 3.63s/it] 79%|███████▉ | 411/520 [25:39<06:36, 3.63s/it] {'loss': 1.2677, 'grad_norm': 0.001553267429358469, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:39<06:36, 3.63s/it] 79%|███████▉ | 412/520 [25:42<06:32, 3.64s/it] {'loss': 1.1781, 'grad_norm': 0.0012820636093235176, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:42<06:32, 3.64s/it] 79%|███████▉ | 413/520 [25:46<06:29, 3.64s/it] {'loss': 1.1798, 'grad_norm': 0.001179828007473557, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:46<06:29, 3.64s/it] 80%|███████▉ | 414/520 [25:50<06:25, 3.63s/it] {'loss': 0.9917, 'grad_norm': 0.00107280428359525, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:50<06:25, 3.63s/it] 80%|███████▉ | 415/520 [25:53<06:24, 3.67s/it] {'loss': 1.15, 'grad_norm': 0.001211366492744208, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:53<06:24, 3.67s/it] 80%|████████ | 416/520 [25:57<06:27, 3.72s/it] {'loss': 1.0726, 'grad_norm': 0.0013495873874091048, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:57<06:27, 3.72s/it] 80%|████████ | 417/520 [26:01<06:25, 3.74s/it] {'loss': 1.2322, 'grad_norm': 0.0013637735927301041, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:01<06:25, 3.74s/it] 80%|████████ | 418/520 [26:05<06:24, 3.77s/it] {'loss': 1.2202, 'grad_norm': 0.0012500098967116555, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:05<06:24, 3.77s/it] 81%|████████ | 419/520 [26:09<06:24, 3.81s/it] {'loss': 1.2105, 'grad_norm': 0.0013977270329279534, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:09<06:24, 3.81s/it] 81%|████████ | 420/520 [26:13<06:22, 3.82s/it] {'loss': 1.1028, 'grad_norm': 0.0013816088183513898, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:13<06:22, 3.82s/it] 81%|████████ | 421/520 [26:16<06:20, 3.84s/it] {'loss': 1.0395, 'grad_norm': 0.0015231064140596678, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:16<06:20, 3.84s/it] 81%|████████ | 422/520 [26:20<06:16, 3.84s/it] {'loss': 1.158, 'grad_norm': 0.0013065944611726189, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:20<06:16, 3.84s/it] 81%|████████▏ | 423/520 [26:24<06:13, 3.85s/it] {'loss': 1.1383, 'grad_norm': 0.0014407876966386149, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:24<06:13, 3.85s/it] 82%|████████▏ | 424/520 [26:28<06:09, 3.85s/it] {'loss': 1.265, 'grad_norm': 0.0013663477921543488, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:28<06:09, 3.85s/it] 82%|████████▏ | 425/520 [26:32<06:05, 3.85s/it] {'loss': 1.1537, 'grad_norm': 0.001265473256648327, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:32<06:05, 3.85s/it] 82%|████████▏ | 426/520 [26:36<05:55, 3.78s/it] {'loss': 1.171, 'grad_norm': 0.0015716859451503063, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:36<05:55, 3.78s/it] 82%|████████▏ | 427/520 [26:39<05:47, 3.74s/it] {'loss': 1.0851, 'grad_norm': 0.001232294767042659, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:39<05:47, 3.74s/it] 82%|████████▏ | 428/520 [26:43<05:41, 3.71s/it] {'loss': 1.0643, 'grad_norm': 0.0013081754535228805, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:43<05:41, 3.71s/it] 82%|████████▎ | 429/520 [26:46<05:36, 3.70s/it] {'loss': 1.1603, 'grad_norm': 0.0012758652791693143, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:46<05:36, 3.70s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:50<05:32, 3.69s/it] {'loss': 1.1641, 'grad_norm': 0.0011984462296609618, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:50<05:32, 3.69s/it] 83%|████████▎ | 431/520 [26:54<05:27, 3.68s/it] {'loss': 1.1496, 'grad_norm': 0.0013612175125410557, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:54<05:27, 3.68s/it] 83%|████████▎ | 432/520 [26:57<05:22, 3.67s/it] {'loss': 1.0715, 'grad_norm': 0.001321575699075261, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:57<05:22, 3.67s/it] 83%|████████▎ | 433/520 [27:01<05:18, 3.66s/it] {'loss': 1.2024, 'grad_norm': 0.001230477811385817, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:01<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:05<05:15, 3.66s/it] {'loss': 0.9562, 'grad_norm': 0.0012975552787464633, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:05<05:15, 3.66s/it] 84%|████████▎ | 435/520 [27:08<05:10, 3.65s/it] {'loss': 1.2432, 'grad_norm': 0.0014625039204558626, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:08<05:10, 3.65s/it] 84%|████████▍ | 436/520 [27:12<05:05, 3.64s/it] {'loss': 1.0426, 'grad_norm': 0.0013349731322673316, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:12<05:05, 3.64s/it] 84%|████████▍ | 437/520 [27:16<05:02, 3.64s/it] {'loss': 1.2644, 'grad_norm': 0.0012984460768615072, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:16<05:02, 3.64s/it] 84%|████████▍ | 438/520 [27:19<04:58, 3.64s/it] {'loss': 1.0814, 'grad_norm': 0.001237099609564276, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:19<04:58, 3.64s/it] 84%|████████▍ | 439/520 [27:23<04:55, 3.65s/it] {'loss': 1.1331, 'grad_norm': 0.0010666492082779479, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:23<04:55, 3.65s/it] 85%|████████▍ | 440/520 [27:27<04:51, 3.64s/it] {'loss': 1.1134, 'grad_norm': 0.001261505267179665, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:27<04:51, 3.64s/it] 85%|████████▍ | 441/520 [27:30<04:47, 3.64s/it] {'loss': 1.147, 'grad_norm': 0.0012616768525708743, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:30<04:47, 3.64s/it] 85%|████████▌ | 442/520 [27:34<04:43, 3.63s/it] {'loss': 1.1819, 'grad_norm': 0.0014365540036165797, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:34<04:43, 3.63s/it] 85%|████████▌ | 443/520 [27:37<04:39, 3.62s/it] {'loss': 1.1906, 'grad_norm': 0.0012553566894682956, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:37<04:39, 3.62s/it] 85%|████████▌ | 444/520 [27:41<04:35, 3.62s/it] {'loss': 1.1567, 'grad_norm': 0.0011738017614383491, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:41<04:35, 3.62s/it] 86%|████████▌ | 445/520 [27:45<04:31, 3.61s/it] {'loss': 1.0853, 'grad_norm': 0.0012346059586415658, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:45<04:31, 3.61s/it] 86%|████████▌ | 446/520 [27:48<04:27, 3.62s/it] {'loss': 1.2268, 'grad_norm': 0.0012733158860256594, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:48<04:27, 3.62s/it] 86%|████████▌ | 447/520 [27:52<04:24, 3.62s/it] {'loss': 1.1665, 'grad_norm': 0.0013094680389424108, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:52<04:24, 3.62s/it] 86%|████████▌ | 448/520 [27:56<04:20, 3.62s/it] {'loss': 1.1557, 'grad_norm': 0.0013211204270049941, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:56<04:20, 3.62s/it] 86%|████████▋ | 449/520 [27:59<04:17, 3.62s/it] {'loss': 1.1825, 'grad_norm': 0.0013301566564286607, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:59<04:17, 3.62s/it] 87%|████████▋ | 450/520 [28:03<04:12, 3.61s/it] {'loss': 1.1875, 'grad_norm': 0.001291000547516858, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:03<04:12, 3.61s/it] 87%|████████▋ | 451/520 [28:06<04:09, 3.61s/it] {'loss': 1.1825, 'grad_norm': 0.001289923904524372, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:06<04:09, 3.61s/it] 87%|████████▋ | 452/520 [28:10<04:05, 3.61s/it] {'loss': 1.2228, 'grad_norm': 0.0011791569285220368, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:10<04:05, 3.61s/it] 87%|████████▋ | 453/520 [28:14<04:02, 3.61s/it] {'loss': 1.1985, 'grad_norm': 0.0012499809813701153, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:14<04:02, 3.61s/it] 87%|████████▋ | 454/520 [28:17<03:58, 3.61s/it] {'loss': 1.0936, 'grad_norm': 0.0013127904492860052, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:17<03:58, 3.61s/it] 88%|████████▊ | 455/520 [28:21<03:54, 3.61s/it] {'loss': 1.2304, 'grad_norm': 0.0012526918410538236, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:21<03:54, 3.61s/it] 88%|████████▊ | 456/520 [28:24<03:50, 3.60s/it] {'loss': 1.153, 'grad_norm': 0.0013042122304427607, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:24<03:50, 3.60s/it] 88%|████████▊ | 457/520 [28:28<03:47, 3.62s/it] {'loss': 1.111, 'grad_norm': 0.001127550935916981, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:28<03:47, 3.62s/it] 88%|████████▊ | 458/520 [28:32<03:44, 3.62s/it] {'loss': 1.2869, 'grad_norm': 0.0013458988756651882, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:32<03:44, 3.62s/it] 88%|████████▊ | 459/520 [28:35<03:41, 3.63s/it] {'loss': 1.218, 'grad_norm': 0.0013352562920277223, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:35<03:41, 3.63s/it] 88%|████████▊ | 460/520 [28:39<03:37, 3.63s/it] {'loss': 1.1033, 'grad_norm': 0.0012802950332702536, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:39<03:37, 3.63s/it] 89%|████████▊ | 461/520 [28:43<03:34, 3.63s/it] {'loss': 1.1946, 'grad_norm': 0.0009954175179476916, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:43<03:34, 3.63s/it] 89%|████████▉ | 462/520 [28:46<03:30, 3.62s/it] {'loss': 1.2703, 'grad_norm': 0.0012248053887450486, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:46<03:30, 3.62s/it] 89%|████████▉ | 463/520 [28:50<03:26, 3.63s/it] {'loss': 1.0626, 'grad_norm': 0.0012943295180453784, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:50<03:26, 3.63s/it] 89%|████████▉ | 464/520 [28:53<03:23, 3.64s/it] {'loss': 1.202, 'grad_norm': 0.0013504693817041162, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:53<03:23, 3.64s/it] 89%|████████▉ | 465/520 [28:57<03:19, 3.63s/it] {'loss': 1.3065, 'grad_norm': 0.0013705338063646562, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:57<03:19, 3.63s/it] 90%|████████▉ | 466/520 [29:01<03:16, 3.63s/it] {'loss': 1.1886, 'grad_norm': 0.0011882055011888108, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:01<03:16, 3.63s/it] 90%|████████▉ | 467/520 [29:04<03:12, 3.63s/it] {'loss': 1.1596, 'grad_norm': 0.0011644996914367022, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:04<03:12, 3.63s/it] 90%|█████████ | 468/520 [29:08<03:08, 3.63s/it] {'loss': 1.1649, 'grad_norm': 0.0014177985681775088, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:08<03:08, 3.63s/it] 90%|█████████ | 469/520 [29:12<03:05, 3.63s/it] {'loss': 1.2251, 'grad_norm': 0.0014108160530483726, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:12<03:05, 3.63s/it] 90%|█████████ | 470/520 [29:15<03:01, 3.63s/it] {'loss': 1.104, 'grad_norm': 0.001191896187345422, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:15<03:01, 3.63s/it] 91%|█████████ | 471/520 [29:19<02:58, 3.64s/it] {'loss': 1.1293, 'grad_norm': 0.0013217411042400495, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:19<02:58, 3.64s/it] 91%|█████████ | 472/520 [29:23<02:55, 3.66s/it] {'loss': 1.0947, 'grad_norm': 0.0013628611232510601, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:23<02:55, 3.66s/it] 91%|█████████ | 473/520 [29:26<02:51, 3.66s/it] {'loss': 1.1602, 'grad_norm': 0.0013215813161755205, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:26<02:51, 3.66s/it] 91%|█████████ | 474/520 [29:30<02:48, 3.66s/it] {'loss': 1.1924, 'grad_norm': 0.0011909518317060937, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:30<02:48, 3.66s/it] 91%|█████████▏| 475/520 [29:34<02:43, 3.64s/it] {'loss': 1.1147, 'grad_norm': 0.001181229315063517, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:34<02:43, 3.64s/it] 92%|█████████▏| 476/520 [29:37<02:39, 3.63s/it] {'loss': 1.1463, 'grad_norm': 0.0013161165281275731, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:37<02:39, 3.63s/it] 92%|█████████▏| 477/520 [29:41<02:35, 3.62s/it] {'loss': 1.14, 'grad_norm': 0.0014320410172148222, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:41<02:35, 3.62s/it] 92%|█████████▏| 478/520 [29:44<02:31, 3.61s/it] {'loss': 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[30:02<02:13, 3.62s/it] {'loss': 1.1611, 'grad_norm': 0.0013875054160370297, 'learning_rate': 0.002647806273887665, 'epoch': 0.93} + 93%|█████████▎| 483/520 [30:02<02:13, 3.62s/it] 93%|█████████▎| 484/520 [30:06<02:10, 3.61s/it] {'loss': 1.1669, 'grad_norm': 0.0013238627988824653, 'learning_rate': 0.0025072087818176383, 'epoch': 0.93} + 93%|█████████▎| 484/520 [30:06<02:10, 3.61s/it] 93%|█████████▎| 485/520 [30:10<02:06, 3.62s/it] {'loss': 1.1218, 'grad_norm': 0.001270323458418536, 'learning_rate': 0.002370399288006664, 'epoch': 0.93} + 93%|█████████▎| 485/520 [30:10<02:06, 3.62s/it] 93%|█████████▎| 486/520 [30:13<02:02, 3.61s/it] {'loss': 1.2439, 'grad_norm': 0.0013620536078594303, 'learning_rate': 0.0022373831080695463, 'epoch': 0.93} + 93%|█████████▎| 486/520 [30:13<02:02, 3.61s/it] 94%|█████████▎| 487/520 [30:17<01:59, 3.61s/it] {'loss': 1.0924, 'grad_norm': 0.001232374822674755, 'learning_rate': 0.0021081654102351635, 'epoch': 0.94} + 94%|█████████▎| 487/520 [30:17<01:59, 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[32:17<00:00, 3.87s/it] 100%|██████████| 520/520 [32:17<00:00, 3.73s/it] +[2025-10-17 18:00:18,717] [INFO] [launch.py:348:main] Process 526472 exits successfully. +[2025-10-17 18:00:18,718] [INFO] [launch.py:348:main] Process 526477 exits successfully. +[2025-10-17 18:00:19,719] [INFO] [launch.py:348:main] Process 526473 exits successfully. +[2025-10-17 18:00:19,720] [INFO] [launch.py:348:main] Process 526474 exits successfully. +[2025-10-17 18:00:19,720] [INFO] [launch.py:348:main] Process 526471 exits successfully. +[2025-10-17 18:00:19,721] [INFO] [launch.py:348:main] Process 526475 exits successfully. +[2025-10-17 18:00:19,721] [INFO] [launch.py:348:main] Process 526476 exits successfully. +[2025-10-17 18:00:23,726] [INFO] [launch.py:348:main] Process 526470 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.7_2e-1_connector-5.0_2.7_2e-1_ablation_20251017_172630.log +Timestamp: 2025-10-17 18:00:26 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation_20251017_180026.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation_20251017_180026.log new file mode 100644 index 0000000000000000000000000000000000000000..f9c1161c996a0b199c8867d5f1be99293fbe3de3 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation_20251017_180026.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation_20251017_180026.log +Timestamp: 2025-10-17 18:00:26 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:00:28,896] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:31,607] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 18:00:31,608] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 5.0 --temperature_attn_text 2.9 --temperature_mlp_text 2.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 5.0 --temperature_attn_vision 2.9 --temperature_mlp_vision 2.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 5.0 --temperature_connector 2.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:00:34,171] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:35,241] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 18:00:35,241] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 18:00:35,241] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 18:00:35,241] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 18:00:35,241] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 18:00:35,241] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 18:00:35,241] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 18:00:35,243] [INFO] [launch.py:253:main] process 548175 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 18:00:35,245] [INFO] [launch.py:253:main] process 548176 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation', '--num_train_epochs', '1', 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['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', 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'--train_data_ratio', '0.1'] +[2025-10-17 18:00:35,256] [INFO] [launch.py:253:main] process 548182 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '5.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '5.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '5.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:00:41,847] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:41,880] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,147] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,188] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,217] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,217] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,220] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,229] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:00:42,255] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,284] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,557] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,603] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,604] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 18:00:42,627] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,629] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,636] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:00:42,642] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.9, 'temperature_mlp': 2.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.9, + "temperature_mlp": 2.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:548175:548175 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548176:548176 [1] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548182:548182 [7] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548178:548178 [3] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548181:548181 [6] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:548179:548179 [4] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 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: 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548180:549774 [5] NCCL INFO ncclCommInitRank comm 0x5575050245c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:548179:549755 [4] NCCL INFO ncclCommInitRank comm 0x558ebb0343a0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548182:549736 [7] NCCL INFO ncclCommInitRank comm 0x56273f7e4660 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548181:549754 [6] NCCL INFO ncclCommInitRank comm 0x55d4f3356f40 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548177:549756 [2] NCCL INFO ncclCommInitRank comm 0x5599ec4ef4f0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548176:549735 [1] NCCL INFO ncclCommInitRank comm 0x55accf7fdf00 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548178:549737 [3] NCCL INFO ncclCommInitRank comm 0x563f25dbf810 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x753efa390e0e806d - Init COMPLETE +ywang29-vrdb-test1-worker-0:548175:549734 [0] NCCL INFO ncclCommInitRank comm 0x55c975cfeb20 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x753efa390e0e806d - Init COMPLETE +[2025-10-17 18:01:27,725] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 18:01:29,456] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=5.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=5.000000 +Pre-training init connector._connector.0.scores: Mean=5.000005 +Pre-training init connector._connector.2.scores: Mean=4.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 18:01:47,569 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 18:01:47,576 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters 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+language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:005->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548175:554681 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548177:554684 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548176:554687 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548179:554688 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548178:554682 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548180:554685 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548182:554683 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:548181:554686 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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2/520 [00:29<1:50:52, 12.84s/it] {'loss': 2.3915, 'grad_norm': 0.03318098938928804, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:29<1:50:52, 12.84s/it] 1%| | 3/520 [00:33<1:14:20, 8.63s/it] {'loss': 2.5844, 'grad_norm': 0.03726681503518878, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:33<1:14:20, 8.63s/it] 1%| | 4/520 [00:36<57:05, 6.64s/it] {'loss': 1.8703, 'grad_norm': 0.013843002482624428, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:36<57:05, 6.64s/it] 1%| | 5/520 [00:40<47:39, 5.55s/it] {'loss': 1.8283, 'grad_norm': 0.009127955653186047, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:40<47:39, 5.55s/it] 1%| | 6/520 [00:44<42:03, 4.91s/it] {'loss': 1.6337, 'grad_norm': 0.008593405935075878, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:44<42:03, 4.91s/it] 1%|▏ | 7/520 [00:47<38:18, 4.48s/it] {'loss': 1.6161, 'grad_norm': 0.010985256264064026, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:47<38:18, 4.48s/it] 2%|▏ | 8/520 [00:51<37:32, 4.40s/it] {'loss': 1.5814, 'grad_norm': 0.006833560394079409, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:51<37:32, 4.40s/it] 2%|▏ | 9/520 [00:55<35:51, 4.21s/it] {'loss': 1.6117, 'grad_norm': 0.003987341253004048, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:55<35:51, 4.21s/it] 2%|▏ | 10/520 [00:59<34:48, 4.09s/it] {'loss': 1.4435, 'grad_norm': 0.004054402121544938, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:59<34:48, 4.09s/it] 2%|▏ | 11/520 [01:03<34:27, 4.06s/it] {'loss': 1.5109, 'grad_norm': 0.004707219183913991, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [01:03<34:27, 4.06s/it] 2%|▏ | 12/520 [01:07<33:45, 3.99s/it] {'loss': 1.444, 'grad_norm': 0.0035890401469049252, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:07<33:45, 3.99s/it][2025-10-17 18:03:03,806] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:11<35:02, 4.15s/it] {'loss': 1.4515, 'grad_norm': 0.002829182307535601, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:11<35:02, 4.15s/it] 3%|▎ | 14/520 [01:15<34:08, 4.05s/it] {'loss': 1.4991, 'grad_norm': 0.0034278944512223844, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:15<34:08, 4.05s/it] 3%|▎ | 15/520 [01:19<33:32, 3.99s/it] {'loss': 1.4844, 'grad_norm': 0.0026395390423634623, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:19<33:32, 3.99s/it] 3%|▎ | 16/520 [01:23<33:07, 3.94s/it] {'loss': 1.4329, 'grad_norm': 0.0024305680916681177, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:23<33:07, 3.94s/it] 3%|▎ | 17/520 [01:27<32:53, 3.92s/it] {'loss': 1.5339, 'grad_norm': 0.0029246911927059117, 'learning_rate': 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0.0012807379945895439, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:42<14:00, 3.79s/it] 57%|█████▊ | 299/520 [18:46<13:59, 3.80s/it] {'loss': 1.2745, 'grad_norm': 0.0012513461357772867, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:46<13:59, 3.80s/it] 58%|█████▊ | 300/520 [18:50<13:55, 3.80s/it] {'loss': 1.301, 'grad_norm': 0.0013252335811176949, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:50<13:55, 3.80s/it] 58%|█████▊ | 301/520 [18:54<13:53, 3.81s/it] {'loss': 1.2781, 'grad_norm': 0.0013132055914654899, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:54<13:53, 3.81s/it] 58%|█████▊ | 302/520 [18:58<13:51, 3.82s/it] {'loss': 1.2907, 'grad_norm': 0.0013296413794857214, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:58<13:51, 3.82s/it] 58%|█████▊ | 303/520 [19:02<13:48, 3.82s/it] {'loss': 1.2078, 'grad_norm': 0.0015247648507122954, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [19:02<13:48, 3.82s/it] 58%|█████▊ | 304/520 [19:06<14:08, 3.93s/it] {'loss': 1.1937, 'grad_norm': 0.0014752321942316914, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:06<14:08, 3.93s/it] 59%|█████▊ | 305/520 [19:10<13:57, 3.89s/it] {'loss': 1.3089, 'grad_norm': 0.0016420898093399941, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:10<13:57, 3.89s/it] 59%|█████▉ | 306/520 [19:13<13:49, 3.88s/it] {'loss': 1.2563, 'grad_norm': 0.0013519024928980803, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:13<13:49, 3.88s/it] 59%|█████▉ | 307/520 [19:17<13:41, 3.86s/it] {'loss': 1.1911, 'grad_norm': 0.0012550594043417192, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:17<13:41, 3.86s/it] 59%|█████▉ | 308/520 [19:21<13:34, 3.84s/it] {'loss': 1.3091, 'grad_norm': 0.0013811640546662744, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:21<13:34, 3.84s/it] 59%|█████▉ | 309/520 [19:25<13:28, 3.83s/it] {'loss': 1.1973, 'grad_norm': 0.0012530149752499572, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:25<13:28, 3.83s/it] 60%|█████▉ | 310/520 [19:29<13:23, 3.83s/it] {'loss': 1.1735, 'grad_norm': 0.001337549196000165, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:29<13:23, 3.83s/it] 60%|█████▉ | 311/520 [19:32<13:19, 3.83s/it] {'loss': 1.1478, 'grad_norm': 0.001368103424954087, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:33<13:19, 3.83s/it] 60%|██████ | 312/520 [19:36<13:15, 3.83s/it] {'loss': 1.1388, 'grad_norm': 0.0015304729681677853, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:36<13:15, 3.83s/it] 60%|██████ | 313/520 [19:40<13:10, 3.82s/it] {'loss': 1.1259, 'grad_norm': 0.0012192856335153507, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:40<13:10, 3.82s/it] 60%|██████ | 314/520 [19:44<13:30, 3.93s/it] {'loss': 1.1668, 'grad_norm': 0.0012635939754382316, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:44<13:30, 3.93s/it] 61%|██████ | 315/520 [19:48<13:21, 3.91s/it] {'loss': 1.2395, 'grad_norm': 0.0015962983149721342, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:48<13:21, 3.91s/it] 61%|██████ | 316/520 [19:53<13:44, 4.04s/it] {'loss': 1.1451, 'grad_norm': 0.0015791340721125085, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:53<13:44, 4.04s/it] 61%|██████ | 317/520 [19:56<13:28, 3.98s/it] {'loss': 1.1577, 'grad_norm': 0.0012571724772752202, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:56<13:28, 3.98s/it] 61%|██████ | 318/520 [20:00<13:15, 3.94s/it] {'loss': 1.2698, 'grad_norm': 0.0014757830071082058, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [20:00<13:15, 3.94s/it] 61%|██████▏ | 319/520 [20:04<13:29, 4.03s/it] {'loss': 1.1462, 'grad_norm': 0.0012396581798291468, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:04<13:29, 4.03s/it] 62%|██████▏ | 320/520 [20:08<13:15, 3.98s/it] {'loss': 1.091, 'grad_norm': 0.0013283150279722866, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:08<13:15, 3.98s/it] 62%|██████▏ | 321/520 [20:12<13:04, 3.94s/it] {'loss': 1.2873, 'grad_norm': 0.0014499319023059992, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:12<13:04, 3.94s/it] 62%|██████▏ | 322/520 [20:16<12:54, 3.91s/it] {'loss': 1.1321, 'grad_norm': 0.0013117326630529326, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:16<12:54, 3.91s/it] 62%|██████▏ | 323/520 [20:20<12:47, 3.90s/it] {'loss': 1.2082, 'grad_norm': 0.001386250337336588, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:20<12:47, 3.90s/it] 62%|██████▏ | 324/520 [20:24<12:40, 3.88s/it] {'loss': 1.2251, 'grad_norm': 0.0013394469553360076, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:24<12:40, 3.88s/it] 62%|██████▎ | 325/520 [20:28<12:36, 3.88s/it] {'loss': 1.231, 'grad_norm': 0.0013314087118311238, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:28<12:36, 3.88s/it] 63%|██████▎ | 326/520 [20:31<12:31, 3.87s/it] {'loss': 1.224, 'grad_norm': 0.0013797926263507288, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:31<12:31, 3.87s/it] 63%|██████▎ | 327/520 [20:35<12:25, 3.86s/it] {'loss': 1.2535, 'grad_norm': 0.0014451256956734887, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:35<12:25, 3.86s/it] 63%|██████▎ | 328/520 [20:39<12:21, 3.86s/it] {'loss': 1.2711, 'grad_norm': 0.0013780715721489574, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:39<12:21, 3.86s/it] 63%|██████▎ | 329/520 [20:43<12:18, 3.86s/it] {'loss': 1.1454, 'grad_norm': 0.0011693114380415006, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:43<12:18, 3.86s/it] 63%|██████▎ | 330/520 [20:47<12:13, 3.86s/it] {'loss': 1.2172, 'grad_norm': 0.0012466660254531446, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:47<12:13, 3.86s/it] 64%|██████▎ | 331/520 [20:51<12:07, 3.85s/it] {'loss': 1.1782, 'grad_norm': 0.0012813584102427473, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:51<12:07, 3.85s/it] 64%|██████▍ | 332/520 [20:54<11:52, 3.79s/it] {'loss': 1.2793, 'grad_norm': 0.0012874251686897614, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:54<11:52, 3.79s/it] 64%|██████▍ | 333/520 [20:58<11:38, 3.73s/it] {'loss': 1.3211, 'grad_norm': 0.0014278586286551652, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:58<11:38, 3.73s/it] 64%|██████▍ | 334/520 [21:02<11:37, 3.75s/it] {'loss': 1.227, 'grad_norm': 0.001402217583915693, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [21:02<11:37, 3.75s/it] 64%|██████▍ | 335/520 [21:05<11:32, 3.75s/it] {'loss': 1.2289, 'grad_norm': 0.0012386455713863623, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:05<11:32, 3.75s/it] 65%|██████▍ | 336/520 [21:09<11:22, 3.71s/it] {'loss': 1.1252, 'grad_norm': 0.0014958051420939514, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:09<11:22, 3.71s/it] 65%|██████▍ | 337/520 [21:13<11:20, 3.72s/it] {'loss': 1.1086, 'grad_norm': 0.0012693369992674264, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:13<11:20, 3.72s/it] 65%|██████▌ | 338/520 [21:17<11:22, 3.75s/it] {'loss': 1.2249, 'grad_norm': 0.0013346375352246082, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:17<11:22, 3.75s/it] 65%|██████▌ | 339/520 [21:20<11:20, 3.76s/it] {'loss': 1.1755, 'grad_norm': 0.0013045781554435127, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:20<11:20, 3.76s/it] 65%|██████▌ | 340/520 [21:24<11:21, 3.79s/it] {'loss': 1.1665, 'grad_norm': 0.0012988864629001375, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:24<11:21, 3.79s/it] 66%|██████▌ | 341/520 [21:28<11:15, 3.77s/it] {'loss': 1.1852, 'grad_norm': 0.0014054035475515691, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:28<11:15, 3.77s/it] 66%|██████▌ | 342/520 [21:32<11:15, 3.79s/it] {'loss': 1.2442, 'grad_norm': 0.0015074644499253405, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:32<11:15, 3.79s/it] 66%|██████▌ | 343/520 [21:36<11:14, 3.81s/it] {'loss': 1.1989, 'grad_norm': 0.001249378532422057, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:36<11:14, 3.81s/it] 66%|██████▌ | 344/520 [21:40<11:21, 3.87s/it] {'loss': 1.1418, 'grad_norm': 0.001283843823599472, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:40<11:21, 3.87s/it] 66%|██████▋ | 345/520 [21:44<11:30, 3.94s/it] {'loss': 1.2552, 'grad_norm': 0.0014114834842077657, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:44<11:30, 3.94s/it] 67%|██████▋ | 346/520 [21:48<11:25, 3.94s/it] {'loss': 1.2097, 'grad_norm': 0.0012462687495793717, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:48<11:25, 3.94s/it] 67%|██████▋ | 347/520 [21:51<11:03, 3.84s/it] {'loss': 1.1579, 'grad_norm': 0.001245321266559738, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:51<11:03, 3.84s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:55<10:50, 3.78s/it] {'loss': 1.1141, 'grad_norm': 0.0015746133031066824, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:55<10:50, 3.78s/it] 67%|██████▋ | 349/520 [21:59<10:46, 3.78s/it] {'loss': 1.1537, 'grad_norm': 0.0013455891886069837, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:59<10:46, 3.78s/it] 67%|██████▋ | 350/520 [22:03<10:39, 3.76s/it] {'loss': 1.1999, 'grad_norm': 0.0014474814324052239, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [22:03<10:39, 3.76s/it] 68%|██████▊ | 351/520 [22:06<10:34, 3.75s/it] {'loss': 1.1072, 'grad_norm': 0.0012686419393972484, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:06<10:34, 3.75s/it] 68%|██████▊ | 352/520 [22:10<10:25, 3.72s/it] {'loss': 1.2307, 'grad_norm': 0.0012897631894968873, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:10<10:25, 3.72s/it] 68%|██████▊ | 353/520 [22:14<10:22, 3.73s/it] {'loss': 1.1637, 'grad_norm': 0.0011164094865374924, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:14<10:22, 3.73s/it] 68%|██████▊ | 354/520 [22:17<10:21, 3.74s/it] {'loss': 1.2857, 'grad_norm': 0.0012205132880813646, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:17<10:21, 3.74s/it] 68%|██████▊ | 355/520 [22:21<10:19, 3.76s/it] {'loss': 1.1693, 'grad_norm': 0.0013119046216216064, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:21<10:19, 3.76s/it] 68%|██████▊ | 356/520 [22:25<10:10, 3.72s/it] {'loss': 1.1691, 'grad_norm': 0.001363520829953335, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:25<10:10, 3.72s/it] 69%|██████▊ | 357/520 [22:29<10:05, 3.71s/it] {'loss': 1.1998, 'grad_norm': 0.001288869533049429, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:29<10:05, 3.71s/it] 69%|██████▉ | 358/520 [22:32<10:05, 3.74s/it] {'loss': 1.1337, 'grad_norm': 0.001314260867037482, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:32<10:05, 3.74s/it] 69%|██████▉ | 359/520 [22:36<10:06, 3.77s/it] {'loss': 1.2158, 'grad_norm': 0.0013200845173636083, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:36<10:06, 3.77s/it] 69%|██████▉ | 360/520 [22:40<10:02, 3.77s/it] {'loss': 1.2246, 'grad_norm': 0.0013444184737285893, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:40<10:02, 3.77s/it] 69%|██████▉ | 361/520 [22:44<10:00, 3.78s/it] {'loss': 1.2337, 'grad_norm': 0.0012661574672115218, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:44<10:00, 3.78s/it] 70%|██████▉ | 362/520 [22:48<09:57, 3.78s/it] {'loss': 1.1871, 'grad_norm': 0.001509650041391153, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:48<09:57, 3.78s/it] 70%|██████▉ | 363/520 [22:51<09:45, 3.73s/it] {'loss': 1.2061, 'grad_norm': 0.0013151262272936318, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:51<09:45, 3.73s/it] 70%|███████ | 364/520 [22:55<09:37, 3.70s/it] {'loss': 1.2545, 'grad_norm': 0.0012615499991397638, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:55<09:37, 3.70s/it] 70%|███████ | 365/520 [22:58<09:33, 3.70s/it] {'loss': 1.2672, 'grad_norm': 0.0014374871287585755, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:58<09:33, 3.70s/it] 70%|███████ | 366/520 [23:02<09:25, 3.67s/it] {'loss': 1.2245, 'grad_norm': 0.0013001810304556845, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [23:02<09:25, 3.67s/it] 71%|███████ | 367/520 [23:06<09:19, 3.66s/it] {'loss': 1.2235, 'grad_norm': 0.0013081884329713327, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:06<09:19, 3.66s/it] 71%|███████ | 368/520 [23:09<09:13, 3.64s/it] {'loss': 1.0758, 'grad_norm': 0.0013722393027263298, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:09<09:13, 3.64s/it] 71%|███████ | 369/520 [23:13<09:08, 3.63s/it] {'loss': 1.2085, 'grad_norm': 0.001170352495957522, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:13<09:08, 3.63s/it] 71%|███████ | 370/520 [23:16<09:02, 3.62s/it] {'loss': 1.1342, 'grad_norm': 0.0012454127419432924, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:16<09:02, 3.62s/it] 71%|███████▏ | 371/520 [23:20<08:58, 3.61s/it] {'loss': 1.135, 'grad_norm': 0.0013500270056369266, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:20<08:58, 3.61s/it] 72%|███████▏ | 372/520 [23:24<08:56, 3.62s/it] {'loss': 1.2864, 'grad_norm': 0.001189054374154919, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:24<08:56, 3.62s/it] 72%|███████▏ | 373/520 [23:27<08:53, 3.63s/it] {'loss': 1.1736, 'grad_norm': 0.0013528298261882771, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:27<08:53, 3.63s/it] 72%|███████▏ | 374/520 [23:31<08:50, 3.63s/it] {'loss': 1.22, 'grad_norm': 0.0012839193676172727, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:31<08:50, 3.63s/it] 72%|███████▏ | 375/520 [23:35<08:47, 3.64s/it] {'loss': 1.1382, 'grad_norm': 0.0013456532375574332, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:35<08:47, 3.64s/it] 72%|███████▏ | 376/520 [23:38<08:44, 3.64s/it] {'loss': 1.255, 'grad_norm': 0.0013020081227229294, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:38<08:44, 3.64s/it] 72%|███████▎ | 377/520 [23:42<08:42, 3.65s/it] {'loss': 1.1842, 'grad_norm': 0.001323470676480542, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:42<08:42, 3.65s/it] 73%|███████▎ | 378/520 [23:46<08:45, 3.70s/it] {'loss': 1.2448, 'grad_norm': 0.001294508606932859, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:46<08:45, 3.70s/it] 73%|███████▎ | 379/520 [23:50<08:46, 3.73s/it] {'loss': 1.2224, 'grad_norm': 0.0012434473425923147, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:50<08:46, 3.73s/it] 73%|███████▎ | 380/520 [23:53<08:47, 3.77s/it] {'loss': 1.2673, 'grad_norm': 0.001312416593162175, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:53<08:47, 3.77s/it] 73%|███████▎ | 381/520 [23:57<08:46, 3.79s/it] {'loss': 1.2246, 'grad_norm': 0.0013081747143484524, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:57<08:46, 3.79s/it] 73%|███████▎ | 382/520 [24:01<08:45, 3.81s/it] {'loss': 1.2213, 'grad_norm': 0.0012798149999781998, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [24:01<08:45, 3.81s/it] 74%|███████▎ | 383/520 [24:05<08:41, 3.81s/it] {'loss': 1.0594, 'grad_norm': 0.0014164647207735215, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [24:05<08:41, 3.81s/it] 74%|███████▍ | 384/520 [24:09<08:38, 3.81s/it] {'loss': 1.2752, 'grad_norm': 0.0011776874746484446, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:09<08:38, 3.81s/it] 74%|███████▍ | 385/520 [24:13<08:33, 3.80s/it] {'loss': 1.1996, 'grad_norm': 0.0012223792762273552, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:13<08:33, 3.80s/it] 74%|███████▍ | 386/520 [24:16<08:30, 3.81s/it] {'loss': 1.1509, 'grad_norm': 0.0011402459400051326, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:16<08:30, 3.81s/it] 74%|███████▍ | 387/520 [24:20<08:26, 3.81s/it] {'loss': 1.2837, 'grad_norm': 0.001243558987856167, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:20<08:26, 3.81s/it] 75%|███████▍ | 388/520 [24:24<08:22, 3.81s/it] {'loss': 1.1037, 'grad_norm': 0.0012215514921279948, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:24<08:22, 3.81s/it] 75%|███████▍ | 389/520 [24:28<08:17, 3.80s/it] {'loss': 1.1547, 'grad_norm': 0.0016790759689550299, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:28<08:17, 3.80s/it] 75%|███████▌ | 390/520 [24:32<08:14, 3.80s/it] {'loss': 1.2205, 'grad_norm': 0.0012488618340669995, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:32<08:14, 3.80s/it] 75%|███████▌ | 391/520 [24:35<08:10, 3.80s/it] {'loss': 1.2962, 'grad_norm': 0.0013565069771786344, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:35<08:10, 3.80s/it] 75%|███████▌ | 392/520 [24:39<08:07, 3.81s/it] {'loss': 1.111, 'grad_norm': 0.001285770566570138, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:39<08:07, 3.81s/it] 76%|███████▌ | 393/520 [24:43<08:01, 3.79s/it] {'loss': 1.1198, 'grad_norm': 0.0011331693932191249, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:43<08:01, 3.79s/it] 76%|███████▌ | 394/520 [24:47<07:52, 3.75s/it] {'loss': 1.1713, 'grad_norm': 0.0014315029168926554, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:47<07:52, 3.75s/it] 76%|███████▌ | 395/520 [24:50<07:43, 3.71s/it] {'loss': 1.138, 'grad_norm': 0.0013670569098483193, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:50<07:43, 3.71s/it] 76%|███████▌ | 396/520 [24:54<07:37, 3.69s/it] {'loss': 1.2209, 'grad_norm': 0.0014082461762768366, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:54<07:37, 3.69s/it] 76%|███████▋ | 397/520 [24:57<07:31, 3.67s/it] {'loss': 1.2002, 'grad_norm': 0.0012476843471846393, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:58<07:31, 3.67s/it] 77%|███████▋ | 398/520 [25:01<07:27, 3.67s/it] {'loss': 1.1985, 'grad_norm': 0.0013501837901588665, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [25:01<07:27, 3.67s/it] 77%|███████▋ | 399/520 [25:05<07:23, 3.66s/it] {'loss': 1.1629, 'grad_norm': 0.0012619102355475031, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [25:05<07:23, 3.66s/it] 77%|███████▋ | 400/520 [25:08<07:18, 3.66s/it] {'loss': 1.1963, 'grad_norm': 0.0011862488934750038, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:08<07:18, 3.66s/it] 77%|███████▋ | 401/520 [25:12<07:14, 3.65s/it] {'loss': 1.0361, 'grad_norm': 0.0014659452684891942, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:12<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:16<07:09, 3.64s/it] {'loss': 1.1558, 'grad_norm': 0.0013307343758441174, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:16<07:09, 3.64s/it] 78%|███████▊ | 403/520 [25:19<07:04, 3.63s/it] {'loss': 1.1828, 'grad_norm': 0.0014402263833056292, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:19<07:04, 3.63s/it] 78%|███████▊ | 404/520 [25:23<06:59, 3.62s/it] {'loss': 1.0896, 'grad_norm': 0.0015547926468312977, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:23<06:59, 3.62s/it] 78%|███████▊ | 405/520 [25:26<06:55, 3.61s/it] {'loss': 1.1731, 'grad_norm': 0.0013395582728327425, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:26<06:55, 3.61s/it] 78%|███████▊ | 406/520 [25:30<06:51, 3.61s/it] {'loss': 1.0938, 'grad_norm': 0.0015139351712695004, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:30<06:51, 3.61s/it] 78%|███████▊ | 407/520 [25:34<06:48, 3.61s/it] {'loss': 1.2672, 'grad_norm': 0.0013245199637133443, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:34<06:48, 3.61s/it] 78%|███████▊ | 408/520 [25:37<06:43, 3.60s/it] {'loss': 1.1685, 'grad_norm': 0.0014259540320353031, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:37<06:43, 3.60s/it] 79%|███████▊ | 409/520 [25:41<06:40, 3.61s/it] {'loss': 1.2878, 'grad_norm': 0.0014008659459313897, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:41<06:40, 3.61s/it] 79%|███████▉ | 410/520 [25:45<06:36, 3.61s/it] {'loss': 1.0204, 'grad_norm': 0.0012815052847631036, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:45<06:36, 3.61s/it] 79%|███████▉ | 411/520 [25:48<06:34, 3.62s/it] {'loss': 1.2669, 'grad_norm': 0.0014695257434583762, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:48<06:34, 3.62s/it] 79%|███████▉ | 412/520 [25:52<06:30, 3.61s/it] {'loss': 1.1777, 'grad_norm': 0.0013008148860759671, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:52<06:30, 3.61s/it] 79%|███████▉ | 413/520 [25:55<06:27, 3.62s/it] {'loss': 1.1885, 'grad_norm': 0.0012021826460218029, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:55<06:27, 3.62s/it] 80%|███████▉ | 414/520 [25:59<06:23, 3.62s/it] {'loss': 0.9962, 'grad_norm': 0.0010817456517611755, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:59<06:23, 3.62s/it] 80%|███████▉ | 415/520 [26:03<06:20, 3.62s/it] {'loss': 1.1538, 'grad_norm': 0.0012174854055688321, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [26:03<06:20, 3.62s/it] 80%|████████ | 416/520 [26:06<06:15, 3.61s/it] {'loss': 1.0751, 'grad_norm': 0.0013818134004348224, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:06<06:15, 3.61s/it] 80%|████████ | 417/520 [26:10<06:12, 3.61s/it] {'loss': 1.2348, 'grad_norm': 0.0013612924683476379, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:10<06:12, 3.61s/it] 80%|████████ | 418/520 [26:13<06:08, 3.62s/it] {'loss': 1.2201, 'grad_norm': 0.0012751791830034503, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:13<06:08, 3.62s/it] 81%|████████ | 419/520 [26:17<06:04, 3.61s/it] {'loss': 1.2116, 'grad_norm': 0.0014060356024631947, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:17<06:04, 3.61s/it] 81%|████████ | 420/520 [26:21<06:01, 3.62s/it] {'loss': 1.1052, 'grad_norm': 0.0013974413796702253, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:21<06:01, 3.62s/it] 81%|████████ | 421/520 [26:24<06:00, 3.64s/it] {'loss': 1.0392, 'grad_norm': 0.001506722064265125, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:24<06:00, 3.64s/it] 81%|████████ | 422/520 [26:28<05:57, 3.65s/it] {'loss': 1.162, 'grad_norm': 0.0013340963244087708, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:28<05:57, 3.65s/it] 81%|████████▏ | 423/520 [26:32<05:53, 3.64s/it] {'loss': 1.141, 'grad_norm': 0.001449655489764296, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:32<05:53, 3.64s/it] 82%|████████▏ | 424/520 [26:35<05:49, 3.64s/it] {'loss': 1.2763, 'grad_norm': 0.001328698568354958, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:35<05:49, 3.64s/it] 82%|████████▏ | 425/520 [26:39<05:45, 3.64s/it] {'loss': 1.1545, 'grad_norm': 0.0012644374095722834, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:39<05:45, 3.64s/it] 82%|████████▏ | 426/520 [26:43<05:40, 3.62s/it] {'loss': 1.1726, 'grad_norm': 0.0016165088317144137, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:43<05:40, 3.62s/it] 82%|████████▏ | 427/520 [26:46<05:37, 3.63s/it] {'loss': 1.085, 'grad_norm': 0.0012387660165462696, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:46<05:37, 3.63s/it] 82%|████████▏ | 428/520 [26:50<05:34, 3.64s/it] {'loss': 1.0673, 'grad_norm': 0.0013489275572751275, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:50<05:34, 3.64s/it] 82%|████████▎ | 429/520 [26:54<05:32, 3.65s/it] {'loss': 1.1609, 'grad_norm': 0.0012873658744267514, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:54<05:32, 3.65s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:57<05:27, 3.64s/it] {'loss': 1.1657, 'grad_norm': 0.0012071222766700134, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:57<05:27, 3.64s/it] 83%|████████▎ | 431/520 [27:01<05:24, 3.65s/it] {'loss': 1.1587, 'grad_norm': 0.0013536383721025995, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [27:01<05:24, 3.65s/it] 83%|████████▎ | 432/520 [27:04<05:20, 3.64s/it] {'loss': 1.0737, 'grad_norm': 0.0013331136542770205, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:04<05:20, 3.64s/it] 83%|████████▎ | 433/520 [27:08<05:16, 3.64s/it] {'loss': 1.2013, 'grad_norm': 0.0012535140670781144, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:08<05:16, 3.64s/it] 83%|████████▎ | 434/520 [27:12<05:12, 3.63s/it] {'loss': 0.9525, 'grad_norm': 0.0013319583518626012, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:12<05:12, 3.63s/it] 84%|████████▎ | 435/520 [27:15<05:08, 3.63s/it] {'loss': 1.2452, 'grad_norm': 0.0014196562287443592, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:15<05:08, 3.63s/it] 84%|████████▍ | 436/520 [27:19<05:04, 3.62s/it] {'loss': 1.0413, 'grad_norm': 0.0013711780100750936, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:19<05:04, 3.62s/it] 84%|████████▍ | 437/520 [27:23<05:00, 3.62s/it] {'loss': 1.2637, 'grad_norm': 0.0013152215571099596, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:23<05:00, 3.62s/it] 84%|████████▍ | 438/520 [27:26<04:57, 3.63s/it] {'loss': 1.0833, 'grad_norm': 0.0012662666279296382, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:26<04:57, 3.63s/it] 84%|████████▍ | 439/520 [27:30<04:53, 3.62s/it] {'loss': 1.1386, 'grad_norm': 0.001074125412327539, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:30<04:53, 3.62s/it] 85%|████████▍ | 440/520 [27:33<04:49, 3.62s/it] {'loss': 1.1143, 'grad_norm': 0.0012882149684863638, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:33<04:49, 3.62s/it] 85%|████████▍ | 441/520 [27:37<04:45, 3.62s/it] {'loss': 1.1665, 'grad_norm': 0.0013009871807847405, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:37<04:45, 3.62s/it] 85%|████████▌ | 442/520 [27:41<04:42, 3.62s/it] {'loss': 1.1849, 'grad_norm': 0.0014291048863759972, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:41<04:42, 3.62s/it] 85%|████████▌ | 443/520 [27:44<04:40, 3.65s/it] {'loss': 1.1954, 'grad_norm': 0.0012943956616061354, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:44<04:40, 3.65s/it] 85%|████████▌ | 444/520 [27:48<04:41, 3.70s/it] {'loss': 1.1595, 'grad_norm': 0.0012211441036271188, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:48<04:41, 3.70s/it] 86%|████████▌ | 445/520 [27:52<04:40, 3.74s/it] {'loss': 1.0864, 'grad_norm': 0.001284839645550388, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:52<04:40, 3.74s/it] 86%|████████▌ | 446/520 [27:56<04:39, 3.77s/it] {'loss': 1.2345, 'grad_norm': 0.0012185814980368932, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:56<04:39, 3.77s/it] 86%|████████▌ | 447/520 [28:00<04:34, 3.76s/it] {'loss': 1.1663, 'grad_norm': 0.0012906232089421091, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [28:00<04:34, 3.76s/it] 86%|████████▌ | 448/520 [28:03<04:27, 3.72s/it] {'loss': 1.1548, 'grad_norm': 0.00137509562660069, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [28:03<04:27, 3.72s/it] 86%|████████▋ | 449/520 [28:07<04:21, 3.69s/it] {'loss': 1.1895, 'grad_norm': 0.0013164729774837525, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:07<04:21, 3.69s/it] 87%|████████▋ | 450/520 [28:10<04:16, 3.66s/it] {'loss': 1.1868, 'grad_norm': 0.0013232725715906055, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:10<04:16, 3.66s/it] 87%|████████▋ | 451/520 [28:14<04:11, 3.64s/it] {'loss': 1.1836, 'grad_norm': 0.0013088934503353942, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:14<04:11, 3.64s/it] 87%|████████▋ | 452/520 [28:18<04:05, 3.62s/it] {'loss': 1.2266, 'grad_norm': 0.001217387674369242, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:18<04:05, 3.62s/it] 87%|████████▋ | 453/520 [28:21<04:01, 3.61s/it] {'loss': 1.2058, 'grad_norm': 0.0012931161785912503, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:21<04:01, 3.61s/it] 87%|████████▋ | 454/520 [28:25<03:58, 3.61s/it] {'loss': 1.0948, 'grad_norm': 0.0013204248348486144, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:25<03:58, 3.61s/it] 88%|████████▊ | 455/520 [28:28<03:54, 3.60s/it] {'loss': 1.2319, 'grad_norm': 0.0013105098344101996, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:28<03:54, 3.60s/it] 88%|████████▊ | 456/520 [28:32<03:50, 3.60s/it] {'loss': 1.1556, 'grad_norm': 0.0013323791023809933, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:32<03:50, 3.60s/it] 88%|████████▊ | 457/520 [28:36<03:49, 3.65s/it] {'loss': 1.1305, 'grad_norm': 0.0011544451073217975, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:36<03:49, 3.65s/it] 88%|████████▊ | 458/520 [28:39<03:46, 3.65s/it] {'loss': 1.2891, 'grad_norm': 0.0014286456655671679, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:39<03:46, 3.65s/it] 88%|████████▊ | 459/520 [28:43<03:42, 3.65s/it] {'loss': 1.2222, 'grad_norm': 0.001353313499605615, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:43<03:42, 3.65s/it] 88%|████████▊ | 460/520 [28:47<03:38, 3.64s/it] {'loss': 1.1088, 'grad_norm': 0.0013242090728504254, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:47<03:38, 3.64s/it] 89%|████████▊ | 461/520 [28:50<03:34, 3.63s/it] {'loss': 1.204, 'grad_norm': 0.0010106453136272272, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:50<03:34, 3.63s/it] 89%|████████▉ | 462/520 [28:54<03:30, 3.62s/it] {'loss': 1.2825, 'grad_norm': 0.0012460952338760793, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:54<03:30, 3.62s/it] 89%|████████▉ | 463/520 [28:57<03:26, 3.61s/it] {'loss': 1.0661, 'grad_norm': 0.0013592766051430895, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:57<03:26, 3.61s/it] 89%|████████▉ | 464/520 [29:01<03:22, 3.62s/it] {'loss': 1.2049, 'grad_norm': 0.0013904734345401679, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [29:01<03:22, 3.62s/it] 89%|████████▉ | 465/520 [29:05<03:19, 3.62s/it] {'loss': 1.3107, 'grad_norm': 0.0014077929873005212, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [29:05<03:19, 3.62s/it] 90%|████████▉ | 466/520 [29:09<03:18, 3.67s/it] {'loss': 1.1911, 'grad_norm': 0.001203689245593698, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:09<03:18, 3.67s/it] 90%|████████▉ | 467/520 [29:12<03:16, 3.72s/it] {'loss': 1.1648, 'grad_norm': 0.0011761896930380146, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:12<03:16, 3.72s/it] 90%|█████████ | 468/520 [29:16<03:14, 3.74s/it] {'loss': 1.168, 'grad_norm': 0.0014510873235118234, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:16<03:14, 3.74s/it] 90%|█████████ | 469/520 [29:20<03:11, 3.76s/it] {'loss': 1.2271, 'grad_norm': 0.0014366833889564875, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:20<03:11, 3.76s/it] 90%|█████████ | 470/520 [29:24<03:08, 3.78s/it] {'loss': 1.1083, 'grad_norm': 0.001188019924475897, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:24<03:08, 3.78s/it] 91%|█████████ | 471/520 [29:28<03:05, 3.79s/it] {'loss': 1.1286, 'grad_norm': 0.0013420315347743364, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:28<03:05, 3.79s/it] 91%|█████████ | 472/520 [29:31<03:02, 3.80s/it] {'loss': 1.0961, 'grad_norm': 0.0014032094604642328, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:31<03:02, 3.80s/it] 91%|█████████ | 473/520 [29:35<02:58, 3.80s/it] {'loss': 1.1642, 'grad_norm': 0.0013264703343606192, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:35<02:58, 3.80s/it] 91%|█████████ | 474/520 [29:39<02:54, 3.79s/it] {'loss': 1.2006, 'grad_norm': 0.001217042227063626, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:39<02:54, 3.79s/it] 91%|█████████▏| 475/520 [29:43<02:50, 3.78s/it] {'loss': 1.1188, 'grad_norm': 0.0011947627471308694, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:43<02:50, 3.78s/it] 92%|█████████▏| 476/520 [29:47<02:46, 3.79s/it] {'loss': 1.1482, 'grad_norm': 0.0013117861453300654, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:47<02:46, 3.79s/it] 92%|█████████▏| 477/520 [29:50<02:41, 3.76s/it] {'loss': 1.1382, 'grad_norm': 0.0014393709832383457, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:50<02:41, 3.76s/it] 92%|█████████▏| 478/520 [29:54<02:36, 3.72s/it] {'loss': 1.0929, 'grad_norm': 0.0012928782499009783, 'learning_rate': 0.00340741737109318, 'epoch': 0.92} + 92%|█████████▏| 478/520 [29:54<02:36, 3.72s/it] 92%|█████████▏| 479/520 [29:57<02:31, 3.69s/it] {'loss': 1.1669, 'grad_norm': 0.0013382353239581037, 'learning_rate': 0.003247964765680389, 'epoch': 0.92} + 92%|█████████▏| 479/520 [29:57<02:31, 3.69s/it] 92%|█████████▏| 480/520 [30:01<02:27, 3.69s/it] {'loss': 1.1891, 'grad_norm': 0.0011841078257989004, 'learning_rate': 0.0030922713770922153, 'epoch': 0.92} + 92%|█████████▏| 480/520 [30:01<02:27, 3.69s/it] 92%|█████████▎| 481/520 [30:05<02:23, 3.67s/it] {'loss': 1.1834, 'grad_norm': 0.0011827443330974928, 'learning_rate': 0.0029403432546609046, 'epoch': 0.93} + 92%|█████████▎| 481/520 [30:05<02:23, 3.67s/it] 93%|█████████▎| 482/520 [30:08<02:18, 3.65s/it] {'loss': 1.1956, 'grad_norm': 0.0012649334446856357, 'learning_rate': 0.0027921863014225504, 'epoch': 0.93} + 93%|█████████▎| 482/520 [30:08<02:18, 3.65s/it] 93%|█████████▎| 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[32:12<00:14, 3.63s/it] 99%|█████████▉| 517/520 [32:16<00:10, 3.62s/it] {'loss': 1.2017, 'grad_norm': 0.0012501095685387622, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:16<00:10, 3.62s/it] 100%|█████████▉| 518/520 [32:19<00:07, 3.60s/it] {'loss': 1.162, 'grad_norm': 0.0013478235260329277, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:19<00:07, 3.60s/it] 100%|█████████▉| 519/520 [32:23<00:03, 3.59s/it] {'loss': 1.1701, 'grad_norm': 0.0012468752420577997, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:23<00:03, 3.59s/it] 100%|██████████| 520/520 [32:27<00:00, 3.85s/it] {'loss': 1.1845, 'grad_norm': 0.001292444289557238, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:27<00:00, 3.85s/it] {'train_runtime': 1947.9054, 'train_samples_per_second': 34.154, 'train_steps_per_second': 0.267, 'train_loss': 1.2675134830749952, 'epoch': 1.0} + 100%|██████████| 520/520 [32:27<00:00, 3.85s/it] 100%|██████████| 520/520 [32:27<00:00, 3.75s/it] +[2025-10-17 18:34:25,405] [INFO] [launch.py:348:main] Process 548176 exits successfully. +[2025-10-17 18:34:26,407] [INFO] [launch.py:348:main] Process 548177 exits successfully. +[2025-10-17 18:34:26,408] [INFO] [launch.py:348:main] Process 548181 exits successfully. +[2025-10-17 18:34:26,408] [INFO] [launch.py:348:main] Process 548182 exits successfully. +[2025-10-17 18:34:26,408] [INFO] [launch.py:348:main] Process 548179 exits successfully. +[2025-10-17 18:34:26,409] [INFO] [launch.py:348:main] Process 548180 exits successfully. +[2025-10-17 18:34:27,410] [INFO] [launch.py:348:main] Process 548178 exits successfully. +[2025-10-17 18:34:30,413] [INFO] [launch.py:348:main] Process 548175 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-5.0_2.9_2e-1_connector-5.0_2.9_2e-1_ablation_20251017_180026.log +Timestamp: 2025-10-17 18:34:32 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation_20251017_183432.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation_20251017_183432.log new file mode 100644 index 0000000000000000000000000000000000000000..0ade78a1b0ab09fce8888fb4c2be52c966c8f96a --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation_20251017_183432.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation_20251017_183432.log +Timestamp: 2025-10-17 18:34:32 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:34:35,587] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:38,535] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 18:34:38,537] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 0.9 --temperature_mlp_text 0.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 0.9 --temperature_mlp_vision 0.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 0.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:34:41,126] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:42,154] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 18:34:42,155] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 18:34:42,155] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 18:34:42,155] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 18:34:42,155] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 18:34:42,155] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 18:34:42,155] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 18:34:42,157] [INFO] [launch.py:253:main] process 569926 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '0.9', '--temperature_mlp_text', '0.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '0.9', '--temperature_mlp_vision', '0.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '0.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 18:34:42,159] [INFO] [launch.py:253:main] process 569927 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation', '--num_train_epochs', '1', 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'--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 18:34:42,163] [INFO] [launch.py:253:main] process 569929 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', 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['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', 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'--train_data_ratio', '0.1'] +[2025-10-17 18:34:42,170] [INFO] [launch.py:253:main] process 569933 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '0.9', '--temperature_mlp_text', '0.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '0.9', '--temperature_mlp_vision', '0.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '0.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 18:34:49,072] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,148] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,376] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,385] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,438] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,439] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,449] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,456] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 18:34:49,509] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,563] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,790] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,795] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,795] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 18:34:49,853] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,853] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,866] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 18:34:49,867] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 0.9, 'temperature_mlp': 0.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 0.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 0.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 0.9, + "temperature_mlp": 0.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:569926:569926 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:569932:569932 [6] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:569929:569929 [3] NCCL INFO NET/Plugin: Using internal network plugin. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:569933:569933 [7] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:569931:569931 [5] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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+ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569928:571550 [2] NCCL INFO ncclCommInitRank comm 0x559e2880cf30 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569926:571526 [0] NCCL INFO ncclCommInitRank comm 0x55655bd59930 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569929:571528 [3] NCCL INFO ncclCommInitRank comm 0x564034e53cc0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569933:571529 [7] NCCL INFO ncclCommInitRank comm 0x55ee4f1a2580 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569932:571527 [6] NCCL INFO ncclCommInitRank comm 0x55fb7174d910 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569931:571530 [5] NCCL INFO ncclCommInitRank comm 0x5605d2f07880 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:569927:571531 [1] NCCL INFO ncclCommInitRank comm 0x5649265e3550 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x7c793496faac9766 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569930:571532 [4] NCCL INFO ncclCommInitRank comm 0x565511c1fc40 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x7c793496faac9766 - Init COMPLETE +[2025-10-17 18:35:35,276] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 18:35:37,030] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 18:35:55,632 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 18:35:55,639 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters 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4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters 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+language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read 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13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569933:576462 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569932:576461 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569928:576464 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569929:576459 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:569930:576458 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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INFO ncclCommInitRank comm 0x7f5aa006af40 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xdaf0413868099555 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569926:576457 [0] NCCL INFO ncclCommInitRank comm 0x7f054006af40 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xdaf0413868099555 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569931:576460 [5] NCCL INFO ncclCommInitRank comm 0x7f4c6406b7a0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xdaf0413868099555 - Init COMPLETE +ywang29-vrdb-test1-worker-0:569927:576463 [1] NCCL INFO ncclCommInitRank comm 0x7f04ec06acf0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xdaf0413868099555 - Init COMPLETE + 0%| | 1/520 [00:16<2:26:02, 16.88s/it] {'loss': 2.0497, 'grad_norm': 0.0, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:16<2:26:02, 16.88s/it] 0%| | 2/520 [00:20<1:18:12, 9.06s/it] {'loss': 2.06, 'grad_norm': 0.0, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:20<1:18:12, 9.06s/it] 1%| | 3/520 [00:24<56:32, 6.56s/it] {'loss': 2.1958, 'grad_norm': 0.0, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:24<56:32, 6.56s/it] 1%| | 4/520 [00:27<46:26, 5.40s/it] {'loss': 2.0688, 'grad_norm': 0.0, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:27<46:26, 5.40s/it] 1%| | 5/520 [00:31<41:06, 4.79s/it] {'loss': 2.2403, 'grad_norm': 0.0, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:31<41:06, 4.79s/it] 1%| | 6/520 [00:35<38:01, 4.44s/it] {'loss': 1.6782, 'grad_norm': 0.0, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:35<38:01, 4.44s/it] 1%|▏ | 7/520 [00:38<35:59, 4.21s/it] {'loss': 2.0829, 'grad_norm': 0.0, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:38<35:59, 4.21s/it] 2%|▏ | 8/520 [00:43<36:18, 4.25s/it] {'loss': 2.0585, 'grad_norm': 0.0, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:43<36:18, 4.25s/it] 2%|▏ | 9/520 [00:47<36:08, 4.24s/it] {'loss': 2.1936, 'grad_norm': 0.0, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:47<36:08, 4.24s/it] 2%|▏ | 10/520 [00:51<34:19, 4.04s/it] {'loss': 2.0887, 'grad_norm': 0.0, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:51<34:19, 4.04s/it] 2%|▏ | 11/520 [00:54<33:22, 3.93s/it] {'loss': 2.0637, 'grad_norm': 0.0, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:54<33:22, 3.93s/it] 2%|▏ | 12/520 [00:58<32:28, 3.84s/it] {'loss': 1.8848, 'grad_norm': 0.0, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:58<32:28, 3.84s/it][2025-10-17 18:37:03,267] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:02<33:33, 3.97s/it] {'loss': 2.0728, 'grad_norm': 0.0, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:02<33:33, 3.97s/it] 3%|▎ | 14/520 [01:06<32:32, 3.86s/it] {'loss': 2.1118, 'grad_norm': 0.0, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:06<32:32, 3.86s/it] 3%|▎ | 15/520 [01:09<31:54, 3.79s/it] {'loss': 1.7478, 'grad_norm': 0.0, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:09<31:54, 3.79s/it] 3%|▎ | 16/520 [01:13<31:23, 3.74s/it] {'loss': 1.8954, 'grad_norm': 0.0, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:13<31:23, 3.74s/it] 3%|▎ | 17/520 [01:17<30:59, 3.70s/it] {'loss': 2.1158, 'grad_norm': 0.0, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:17<30:59, 3.70s/it] 3%|▎ | 18/520 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[20:22<12:18, 3.83s/it] {'loss': 2.061, 'grad_norm': 0.0, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:22<12:18, 3.83s/it] 63%|██████▎ | 328/520 [20:26<12:13, 3.82s/it] {'loss': 2.1111, 'grad_norm': 0.0, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:26<12:13, 3.82s/it] 63%|██████▎ | 329/520 [20:30<12:07, 3.81s/it] {'loss': 1.9436, 'grad_norm': 0.0, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:30<12:07, 3.81s/it] 63%|██████▎ | 330/520 [20:33<12:05, 3.82s/it] {'loss': 2.1281, 'grad_norm': 0.0, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:33<12:05, 3.82s/it] 64%|██████▎ | 331/520 [20:37<12:01, 3.82s/it] {'loss': 2.1551, 'grad_norm': 0.0, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:37<12:01, 3.82s/it] 64%|██████▍ | 332/520 [20:41<11:57, 3.82s/it] {'loss': 1.8491, 'grad_norm': 0.0, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:41<11:57, 3.82s/it] 64%|██████▍ | 333/520 [20:45<11:54, 3.82s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:45<11:54, 3.82s/it] 64%|██████▍ | 334/520 [20:49<11:51, 3.83s/it] {'loss': 2.1157, 'grad_norm': 0.0, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:49<11:51, 3.83s/it] 64%|██████▍ | 335/520 [20:53<11:50, 3.84s/it] {'loss': 2.013, 'grad_norm': 0.0, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:53<11:50, 3.84s/it] 65%|██████▍ | 336/520 [20:56<11:47, 3.85s/it] {'loss': 2.1874, 'grad_norm': 0.0, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:56<11:47, 3.85s/it] 65%|██████▍ | 337/520 [21:00<11:43, 3.84s/it] {'loss': 2.2477, 'grad_norm': 0.0, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:00<11:43, 3.84s/it] 65%|██████▌ | 338/520 [21:04<11:40, 3.85s/it] {'loss': 2.1774, 'grad_norm': 0.0, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:04<11:40, 3.85s/it] 65%|██████▌ | 339/520 [21:08<11:38, 3.86s/it] {'loss': 2.126, 'grad_norm': 0.0, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:08<11:38, 3.86s/it] 65%|██████▌ | 340/520 [21:12<11:34, 3.86s/it] {'loss': 2.0845, 'grad_norm': 0.0, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:12<11:34, 3.86s/it] 66%|██████▌ | 341/520 [21:16<11:28, 3.85s/it] {'loss': 2.094, 'grad_norm': 0.0, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:16<11:28, 3.85s/it] 66%|██████▌ | 342/520 [21:19<11:24, 3.85s/it] {'loss': 2.0199, 'grad_norm': 0.0, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:19<11:24, 3.85s/it] 66%|██████▌ | 343/520 [21:23<11:22, 3.86s/it] {'loss': 1.7182, 'grad_norm': 0.0, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:23<11:22, 3.86s/it] 66%|██████▌ | 344/520 [21:27<11:17, 3.85s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:27<11:17, 3.85s/it] 66%|██████▋ | 345/520 [21:31<11:15, 3.86s/it] {'loss': 2.2588, 'grad_norm': 0.0, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:31<11:15, 3.86s/it] 67%|██████▋ | 346/520 [21:35<11:14, 3.87s/it] {'loss': 1.859, 'grad_norm': 0.0, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:35<11:14, 3.87s/it] 67%|██████▋ | 347/520 [21:39<11:12, 3.89s/it] {'loss': 1.9277, 'grad_norm': 0.0, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:39<11:12, 3.89s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:43<11:04, 3.86s/it] {'loss': 2.405, 'grad_norm': 0.0, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:43<11:04, 3.86s/it] 67%|██████▋ | 349/520 [21:46<10:48, 3.79s/it] {'loss': 2.2236, 'grad_norm': 0.0, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:46<10:48, 3.79s/it] 67%|██████▋ | 350/520 [21:50<10:39, 3.76s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:50<10:39, 3.76s/it] 68%|██████▊ | 351/520 [21:54<10:37, 3.77s/it] {'loss': 2.0414, 'grad_norm': 0.0, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:54<10:37, 3.77s/it] 68%|██████▊ | 352/520 [21:58<10:29, 3.75s/it] {'loss': 2.0824, 'grad_norm': 0.0, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:58<10:29, 3.75s/it] 68%|██████▊ | 353/520 [22:01<10:21, 3.72s/it] {'loss': 1.8115, 'grad_norm': 0.0, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:01<10:21, 3.72s/it] 68%|██████▊ | 354/520 [22:05<10:12, 3.69s/it] {'loss': 1.8797, 'grad_norm': 0.0, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:05<10:12, 3.69s/it] 68%|██████▊ | 355/520 [22:08<10:05, 3.67s/it] {'loss': 2.0561, 'grad_norm': 0.0, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:08<10:05, 3.67s/it] 68%|██████▊ | 356/520 [22:12<10:03, 3.68s/it] {'loss': 2.2531, 'grad_norm': 0.0, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:12<10:03, 3.68s/it] 69%|██████▊ | 357/520 [22:16<10:02, 3.70s/it] {'loss': 2.0294, 'grad_norm': 0.0, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:16<10:02, 3.70s/it] 69%|██████▉ | 358/520 [22:20<10:01, 3.71s/it] {'loss': 2.0531, 'grad_norm': 0.0, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:20<10:01, 3.71s/it] 69%|██████▉ | 359/520 [22:23<10:03, 3.75s/it] {'loss': 2.011, 'grad_norm': 0.0, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:23<10:03, 3.75s/it] 69%|██████▉ | 360/520 [22:27<10:03, 3.77s/it] {'loss': 1.9948, 'grad_norm': 0.0, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:27<10:03, 3.77s/it] 69%|██████▉ | 361/520 [22:31<10:01, 3.78s/it] {'loss': 1.7504, 'grad_norm': 0.0, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:31<10:01, 3.78s/it] 70%|██████▉ | 362/520 [22:35<09:59, 3.79s/it] {'loss': 2.2058, 'grad_norm': 0.0, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:35<09:59, 3.79s/it] 70%|██████▉ | 363/520 [22:39<09:58, 3.81s/it] {'loss': 2.0753, 'grad_norm': 0.0, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:39<09:58, 3.81s/it] 70%|███████ | 364/520 [22:43<09:56, 3.82s/it] {'loss': 1.9811, 'grad_norm': 0.0, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:43<09:56, 3.82s/it] 70%|███████ | 365/520 [22:46<09:51, 3.82s/it] {'loss': 2.1137, 'grad_norm': 0.0, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:46<09:51, 3.82s/it] 70%|███████ | 366/520 [22:50<09:51, 3.84s/it] {'loss': 2.1027, 'grad_norm': 0.0, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:50<09:51, 3.84s/it] 71%|███████ | 367/520 [22:54<09:51, 3.87s/it] {'loss': 2.1701, 'grad_norm': 0.0, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:54<09:51, 3.87s/it] 71%|███████ | 368/520 [22:58<09:41, 3.82s/it] {'loss': 2.1175, 'grad_norm': 0.0, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:58<09:41, 3.82s/it] 71%|███████ | 369/520 [23:02<09:34, 3.80s/it] {'loss': 1.7789, 'grad_norm': 0.0, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:02<09:34, 3.80s/it] 71%|███████ | 370/520 [23:05<09:27, 3.78s/it] {'loss': 2.0015, 'grad_norm': 0.0, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:05<09:27, 3.78s/it] 71%|███████▏ | 371/520 [23:09<09:22, 3.78s/it] {'loss': 2.1704, 'grad_norm': 0.0, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:09<09:22, 3.78s/it] 72%|███████▏ | 372/520 [23:13<09:16, 3.76s/it] {'loss': 1.8294, 'grad_norm': 0.0, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:13<09:16, 3.76s/it] 72%|███████▏ | 373/520 [23:17<09:12, 3.76s/it] {'loss': 2.0181, 'grad_norm': 0.0, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:17<09:12, 3.76s/it] 72%|███████▏ | 374/520 [23:20<09:09, 3.77s/it] {'loss': 2.1018, 'grad_norm': 0.0, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:20<09:09, 3.77s/it] 72%|███████▏ | 375/520 [23:24<09:06, 3.77s/it] {'loss': 2.1132, 'grad_norm': 0.0, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:24<09:06, 3.77s/it] 72%|███████▏ | 376/520 [23:28<08:59, 3.75s/it] {'loss': 2.0573, 'grad_norm': 0.0, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:28<08:59, 3.75s/it] 72%|███████▎ | 377/520 [23:32<08:57, 3.76s/it] {'loss': 2.0899, 'grad_norm': 0.0, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:32<08:57, 3.76s/it] 73%|███████▎ | 378/520 [23:35<08:52, 3.75s/it] {'loss': 2.0289, 'grad_norm': 0.0, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:35<08:52, 3.75s/it] 73%|███████▎ | 379/520 [23:39<08:48, 3.75s/it] {'loss': 1.9774, 'grad_norm': 0.0, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:39<08:48, 3.75s/it] 73%|███████▎ | 380/520 [23:43<08:40, 3.72s/it] {'loss': 1.8319, 'grad_norm': 0.0, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:43<08:40, 3.72s/it] 73%|███████▎ | 381/520 [23:47<08:38, 3.73s/it] {'loss': 2.0371, 'grad_norm': 0.0, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:47<08:38, 3.73s/it] 73%|███████▎ | 382/520 [23:50<08:32, 3.72s/it] {'loss': 1.9153, 'grad_norm': 0.0, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:50<08:32, 3.72s/it] 74%|███████▎ | 383/520 [23:54<08:27, 3.70s/it] {'loss': 2.2443, 'grad_norm': 0.0, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:54<08:27, 3.70s/it] 74%|███████▍ | 384/520 [23:58<08:22, 3.70s/it] {'loss': 1.6572, 'grad_norm': 0.0, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:58<08:22, 3.70s/it] 74%|███████▍ | 385/520 [24:01<08:22, 3.72s/it] {'loss': 1.9484, 'grad_norm': 0.0, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:01<08:22, 3.72s/it] 74%|███████▍ | 386/520 [24:05<08:17, 3.72s/it] {'loss': 2.0001, 'grad_norm': 0.0, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:05<08:17, 3.72s/it] 74%|███████▍ | 387/520 [24:09<08:11, 3.69s/it] {'loss': 1.7967, 'grad_norm': 0.0, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:09<08:11, 3.69s/it] 75%|███████▍ | 388/520 [24:12<08:05, 3.68s/it] {'loss': 2.1252, 'grad_norm': 0.0, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:12<08:05, 3.68s/it] 75%|███████▍ | 389/520 [24:16<07:59, 3.66s/it] {'loss': 2.2819, 'grad_norm': 0.0, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:16<07:59, 3.66s/it] 75%|███████▌ | 390/520 [24:20<07:55, 3.66s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:20<07:55, 3.66s/it] 75%|███████▌ | 391/520 [24:23<07:54, 3.68s/it] {'loss': 2.0751, 'grad_norm': 0.0, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:23<07:54, 3.68s/it] 75%|███████▌ | 392/520 [24:27<07:49, 3.67s/it] {'loss': 2.0834, 'grad_norm': 0.0, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:27<07:49, 3.67s/it] 76%|███████▌ | 393/520 [24:31<07:45, 3.67s/it] {'loss': 1.6935, 'grad_norm': 0.0, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:31<07:45, 3.67s/it] 76%|███████▌ | 394/520 [24:34<07:41, 3.67s/it] {'loss': 2.1218, 'grad_norm': 0.0, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:34<07:41, 3.67s/it] 76%|███████▌ | 395/520 [24:38<07:38, 3.66s/it] {'loss': 2.1493, 'grad_norm': 0.0, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:38<07:38, 3.66s/it] 76%|███████▌ | 396/520 [24:42<07:33, 3.66s/it] {'loss': 2.0961, 'grad_norm': 0.0, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:42<07:33, 3.66s/it] 76%|███████▋ | 397/520 [24:45<07:30, 3.67s/it] {'loss': 2.0472, 'grad_norm': 0.0, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:45<07:30, 3.67s/it] 77%|███████▋ | 398/520 [24:49<07:26, 3.66s/it] {'loss': 2.2098, 'grad_norm': 0.0, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:49<07:26, 3.66s/it] 77%|███████▋ | 399/520 [24:53<07:24, 3.68s/it] {'loss': 1.8453, 'grad_norm': 0.0, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:53<07:24, 3.68s/it] 77%|███████▋ | 400/520 [24:56<07:21, 3.68s/it] {'loss': 1.8965, 'grad_norm': 0.0, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:56<07:21, 3.68s/it] 77%|███████▋ | 401/520 [25:00<07:17, 3.68s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:00<07:17, 3.68s/it] 77%|███████▋ | 402/520 [25:04<07:13, 3.68s/it] {'loss': 2.1351, 'grad_norm': 0.0, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:04<07:13, 3.68s/it] 78%|███████▊ | 403/520 [25:07<07:09, 3.67s/it] {'loss': 2.1219, 'grad_norm': 0.0, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:07<07:09, 3.67s/it] 78%|███████▊ | 404/520 [25:11<07:04, 3.66s/it] {'loss': 2.2818, 'grad_norm': 0.0, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:11<07:04, 3.66s/it] 78%|███████▊ | 405/520 [25:15<07:03, 3.68s/it] {'loss': 1.875, 'grad_norm': 0.0, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:15<07:03, 3.68s/it] 78%|███████▊ | 406/520 [25:18<06:59, 3.68s/it] {'loss': 2.1806, 'grad_norm': 0.0, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:18<06:59, 3.68s/it] 78%|███████▊ | 407/520 [25:22<06:56, 3.69s/it] {'loss': 2.0986, 'grad_norm': 0.0, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:22<06:56, 3.69s/it] 78%|███████▊ | 408/520 [25:26<06:49, 3.66s/it] {'loss': 2.1517, 'grad_norm': 0.0, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:26<06:49, 3.66s/it] 79%|███████▊ | 409/520 [25:29<06:45, 3.65s/it] {'loss': 2.2385, 'grad_norm': 0.0, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:29<06:45, 3.65s/it] 79%|███████▉ | 410/520 [25:33<06:41, 3.65s/it] {'loss': 2.1727, 'grad_norm': 0.0, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:33<06:41, 3.65s/it] 79%|███████▉ | 411/520 [25:37<06:37, 3.64s/it] {'loss': 2.195, 'grad_norm': 0.0, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:37<06:37, 3.64s/it] 79%|███████▉ | 412/520 [25:40<06:33, 3.64s/it] {'loss': 2.0965, 'grad_norm': 0.0, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:40<06:33, 3.64s/it] 79%|███████▉ | 413/520 [25:44<06:30, 3.65s/it] {'loss': 1.916, 'grad_norm': 0.0, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:44<06:30, 3.65s/it] 80%|███████▉ | 414/520 [25:48<06:28, 3.66s/it] {'loss': 1.757, 'grad_norm': 0.0, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:48<06:28, 3.66s/it] 80%|███████▉ | 415/520 [25:51<06:23, 3.65s/it] {'loss': 2.0894, 'grad_norm': 0.0, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:51<06:23, 3.65s/it] 80%|████████ | 416/520 [25:55<06:19, 3.65s/it] {'loss': 2.3404, 'grad_norm': 0.0, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:55<06:19, 3.65s/it] 80%|████████ | 417/520 [25:59<06:15, 3.64s/it] {'loss': 2.0376, 'grad_norm': 0.0, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:59<06:15, 3.64s/it] 80%|████████ | 418/520 [26:02<06:11, 3.65s/it] {'loss': 1.9876, 'grad_norm': 0.0, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:02<06:11, 3.65s/it] 81%|████████ | 419/520 [26:06<06:08, 3.65s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:06<06:08, 3.65s/it] 81%|████████ | 420/520 [26:10<06:05, 3.66s/it] {'loss': 2.1783, 'grad_norm': 0.0, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:10<06:05, 3.66s/it] 81%|████████ | 421/520 [26:13<06:02, 3.66s/it] {'loss': 2.3788, 'grad_norm': 0.0, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:13<06:02, 3.66s/it] 81%|████████ | 422/520 [26:17<05:58, 3.66s/it] {'loss': 2.1751, 'grad_norm': 0.0, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:17<05:58, 3.66s/it] 81%|████████▏ | 423/520 [26:21<05:54, 3.65s/it] {'loss': 2.3239, 'grad_norm': 0.0, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:21<05:54, 3.65s/it] 82%|████████▏ | 424/520 [26:24<05:51, 3.66s/it] {'loss': 1.8431, 'grad_norm': 0.0, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:24<05:51, 3.66s/it] 82%|████████▏ | 425/520 [26:28<05:46, 3.65s/it] {'loss': 2.0338, 'grad_norm': 0.0, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:28<05:46, 3.65s/it] 82%|████████▏ | 426/520 [26:31<05:42, 3.65s/it] {'loss': 2.2809, 'grad_norm': 0.0, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:31<05:42, 3.65s/it] 82%|████████▏ | 427/520 [26:35<05:38, 3.64s/it] {'loss': 1.9615, 'grad_norm': 0.0, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:35<05:38, 3.64s/it] 82%|████████▏ | 428/520 [26:39<05:34, 3.64s/it] {'loss': 2.179, 'grad_norm': 0.0, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:39<05:34, 3.64s/it] 82%|████████▎ | 429/520 [26:42<05:31, 3.64s/it] {'loss': 2.1882, 'grad_norm': 0.0, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:42<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:46<05:28, 3.65s/it] {'loss': 2.0206, 'grad_norm': 0.0, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:46<05:28, 3.65s/it] 83%|████████▎ | 431/520 [26:50<05:25, 3.66s/it] {'loss': 1.8737, 'grad_norm': 0.0, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:50<05:25, 3.66s/it] 83%|████████▎ | 432/520 [26:53<05:22, 3.66s/it] {'loss': 2.0893, 'grad_norm': 0.0, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:53<05:22, 3.66s/it] 83%|████████▎ | 433/520 [26:57<05:18, 3.66s/it] {'loss': 2.1446, 'grad_norm': 0.0, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:57<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:01<05:14, 3.65s/it] {'loss': 2.162, 'grad_norm': 0.0, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:01<05:14, 3.65s/it] 84%|████████▎ | 435/520 [27:04<05:09, 3.64s/it] {'loss': 2.1714, 'grad_norm': 0.0, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:04<05:09, 3.64s/it] 84%|████████▍ | 436/520 [27:08<05:06, 3.65s/it] {'loss': 2.1073, 'grad_norm': 0.0, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:08<05:06, 3.65s/it] 84%|████████▍ | 437/520 [27:12<05:02, 3.65s/it] {'loss': 2.1399, 'grad_norm': 0.0, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:12<05:02, 3.65s/it] 84%|████████▍ | 438/520 [27:15<04:59, 3.65s/it] {'loss': 2.1034, 'grad_norm': 0.0, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:15<04:59, 3.65s/it] 84%|████████▍ | 439/520 [27:19<04:55, 3.65s/it] {'loss': 1.7664, 'grad_norm': 0.0, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:19<04:55, 3.65s/it] 85%|████████▍ | 440/520 [27:23<04:51, 3.64s/it] {'loss': 2.0058, 'grad_norm': 0.0, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:23<04:51, 3.64s/it] 85%|████████▍ | 441/520 [27:26<04:47, 3.64s/it] {'loss': 1.8248, 'grad_norm': 0.0, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:26<04:47, 3.64s/it] 85%|████████▌ | 442/520 [27:30<04:43, 3.63s/it] {'loss': 2.3179, 'grad_norm': 0.0, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:30<04:43, 3.63s/it] 85%|████████▌ | 443/520 [27:33<04:40, 3.64s/it] {'loss': 2.0141, 'grad_norm': 0.0, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:33<04:40, 3.64s/it] 85%|████████▌ | 444/520 [27:37<04:36, 3.63s/it] {'loss': 1.9937, 'grad_norm': 0.0, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:37<04:36, 3.63s/it] 86%|████████▌ | 445/520 [27:41<04:34, 3.66s/it] {'loss': 1.9637, 'grad_norm': 0.0, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:41<04:34, 3.66s/it] 86%|████████▌ | 446/520 [27:45<04:34, 3.71s/it] {'loss': 1.8401, 'grad_norm': 0.0, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:45<04:34, 3.71s/it] 86%|████████▌ | 447/520 [27:48<04:33, 3.75s/it] {'loss': 2.1475, 'grad_norm': 0.0, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:48<04:33, 3.75s/it] 86%|████████▌ | 448/520 [27:52<04:30, 3.76s/it] {'loss': 2.0884, 'grad_norm': 0.0, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:52<04:30, 3.76s/it] 86%|████████▋ | 449/520 [27:56<04:27, 3.77s/it] {'loss': 1.9783, 'grad_norm': 0.0, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:56<04:27, 3.77s/it] 87%|████████▋ | 450/520 [28:00<04:24, 3.78s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:00<04:24, 3.78s/it] 87%|████████▋ | 451/520 [28:04<04:21, 3.79s/it] {'loss': 2.1608, 'grad_norm': 0.0, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:04<04:21, 3.79s/it] 87%|████████▋ | 452/520 [28:07<04:14, 3.74s/it] {'loss': 1.8367, 'grad_norm': 0.0, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:07<04:14, 3.74s/it] 87%|████████▋ | 453/520 [28:11<04:09, 3.72s/it] {'loss': 1.9767, 'grad_norm': 0.0, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:11<04:09, 3.72s/it] 87%|████████▋ | 454/520 [28:15<04:05, 3.71s/it] {'loss': 2.0911, 'grad_norm': 0.0, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:15<04:05, 3.71s/it] 88%|████████▊ | 455/520 [28:18<04:00, 3.70s/it] {'loss': 2.0563, 'grad_norm': 0.0, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:18<04:00, 3.70s/it] 88%|████████▊ | 456/520 [28:22<03:55, 3.69s/it] {'loss': 2.0794, 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[launch.py:348:main] Process 569933 exits successfully. +[2025-10-17 19:08:32,329] [INFO] [launch.py:348:main] Process 569926 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_0.9_2e-1_connector-7.0_0.9_2e-1_ablation_20251017_183432.log +Timestamp: 2025-10-17 19:08:34 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation_20251017_190835.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation_20251017_190835.log new file mode 100644 index 0000000000000000000000000000000000000000..0e5819e073b323d69dafb5a7b082310cd826e191 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation_20251017_190835.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation_20251017_190835.log +Timestamp: 2025-10-17 19:08:35 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:08:37,625] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:40,685] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 19:08:40,686] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 1.1 --temperature_mlp_text 1.1 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 1.1 --temperature_mlp_vision 1.1 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 1.1 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:08:43,234] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:44,302] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 19:08:44,302] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 19:08:44,302] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 19:08:44,302] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 19:08:44,302] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 19:08:44,302] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 19:08:44,302] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 19:08:44,304] [INFO] [launch.py:253:main] process 591713 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,306] [INFO] [launch.py:253:main] process 591714 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,308] [INFO] [launch.py:253:main] process 591715 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,310] [INFO] [launch.py:253:main] process 591716 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,312] [INFO] [launch.py:253:main] process 591717 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,313] [INFO] [launch.py:253:main] process 591718 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,315] [INFO] [launch.py:253:main] process 591719 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:08:44,317] [INFO] [launch.py:253:main] process 591720 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:08:51,345] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,345] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,345] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,345] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,347] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,355] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,378] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,384] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:08:51,888] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,888] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:08:51,889] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: Apply masks for the following modules: ['llm', 'connector']['llm', 'connector'] + +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.1, 'temperature_mlp': 1.1, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.1, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.1, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.1, + "temperature_mlp": 1.1, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO comm 0x5572ea3c16a0 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO comm 0x55a16b4a7510 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591718:593323 [5] NCCL INFO ncclCommInitRank comm 0x55e73f5d5a90 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591719:593325 [6] NCCL INFO ncclCommInitRank comm 0x55b8025baa40 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591717:593324 [4] NCCL INFO ncclCommInitRank comm 0x55cfc0b93ee0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591720:593322 [7] NCCL INFO ncclCommInitRank comm 0x5572ea3c16a0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591713:593321 [0] NCCL INFO ncclCommInitRank comm 0x55a16b4a7510 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591716:593326 [3] NCCL INFO ncclCommInitRank comm 0x5561d3987660 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591714:593328 [1] NCCL INFO ncclCommInitRank comm 0x55fae0f94020 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xd1179a92272db909 - Init COMPLETE +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:591715:593327 [2] NCCL INFO ncclCommInitRank comm 0x564390e14000 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xd1179a92272db909 - Init COMPLETE +[2025-10-17 19:09:34,670] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 19:09:36,425] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 19:09:54,424 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 19:09:54,431 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:006->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:591720:598226 [7] NCCL INFO ncclCommInitRank comm 0x7f528806aa50 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591717:598228 [4] NCCL INFO ncclCommInitRank comm 0x7f1e2006ac00 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591719:598229 [6] NCCL INFO ncclCommInitRank comm 0x7f4a2406a9b0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591715:598224 [2] NCCL INFO ncclCommInitRank comm 0x7fd31006ab10 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591713:598223 [0] NCCL INFO ncclCommInitRank comm 0x7f2f1806ad20 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591714:598230 [1] NCCL INFO ncclCommInitRank comm 0x7f59a806ad00 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591718:598227 [5] NCCL INFO ncclCommInitRank comm 0x7f8a2006b410 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x8a3c7c90005d53bf - Init COMPLETE +ywang29-vrdb-test1-worker-0:591716:598225 [3] NCCL INFO ncclCommInitRank comm 0x7ff06006a9b0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x8a3c7c90005d53bf - Init COMPLETE + 0%| | 1/520 [00:19<2:46:44, 19.28s/it] {'loss': 2.0497, 'grad_norm': 0.0, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:19<2:46:44, 19.28s/it] 0%| | 2/520 [00:23<1:28:05, 10.20s/it] {'loss': 2.06, 'grad_norm': 0.0, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:23<1:28:05, 10.20s/it] 1%| | 3/520 [00:26<1:02:49, 7.29s/it] {'loss': 2.1958, 'grad_norm': 0.0, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:26<1:02:49, 7.29s/it] 1%| | 4/520 [00:30<51:11, 5.95s/it] {'loss': 2.0688, 'grad_norm': 0.0, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:30<51:11, 5.95s/it] 1%| | 5/520 [00:34<44:17, 5.16s/it] {'loss': 2.2403, 'grad_norm': 0.0, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:34<44:17, 5.16s/it] 1%| | 6/520 [00:38<39:54, 4.66s/it] {'loss': 1.6782, 'grad_norm': 0.0, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:38<39:54, 4.66s/it] 1%|▏ | 7/520 [00:41<36:58, 4.32s/it] {'loss': 2.0829, 'grad_norm': 0.0, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:41<36:58, 4.32s/it] 2%|▏ | 8/520 [00:46<36:41, 4.30s/it] {'loss': 2.0585, 'grad_norm': 0.0, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:46<36:41, 4.30s/it] 2%|▏ | 9/520 [00:50<36:21, 4.27s/it] {'loss': 2.1936, 'grad_norm': 0.0, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:50<36:21, 4.27s/it] 2%|▏ | 10/520 [00:53<34:30, 4.06s/it] {'loss': 2.0887, 'grad_norm': 0.0, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:53<34:30, 4.06s/it] 2%|▏ | 11/520 [00:57<33:35, 3.96s/it] {'loss': 2.0637, 'grad_norm': 0.0, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:57<33:35, 3.96s/it] 2%|▏ | 12/520 [01:01<32:32, 3.84s/it] {'loss': 1.8848, 'grad_norm': 0.0, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:01<32:32, 3.84s/it][2025-10-17 19:11:04,313] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:05<33:43, 3.99s/it] {'loss': 2.0728, 'grad_norm': 0.0, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:05<33:43, 3.99s/it] 3%|▎ | 14/520 [01:09<32:43, 3.88s/it] {'loss': 2.1118, 'grad_norm': 0.0, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:09<32:43, 3.88s/it] 3%|▎ | 15/520 [01:12<31:59, 3.80s/it] {'loss': 1.7478, 'grad_norm': 0.0, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:12<31:59, 3.80s/it] 3%|▎ | 16/520 [01:16<31:25, 3.74s/it] {'loss': 1.8954, 'grad_norm': 0.0, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:16<31:25, 3.74s/it] 3%|▎ | 17/520 [01:20<31:05, 3.71s/it] {'loss': 2.1158, 'grad_norm': 0.0, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:20<31:05, 3.71s/it] 3%|▎ | 18/520 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0.0, 'learning_rate': 0.08756562953525152, 'epoch': 0.55} + 55%|█████▌ | 288/520 [18:06<14:09, 3.66s/it] 56%|█████▌ | 289/520 [18:10<14:04, 3.65s/it] {'loss': 2.2023, 'grad_norm': 0.0, 'learning_rate': 0.08694738077799487, 'epoch': 0.56} + 56%|█████▌ | 289/520 [18:10<14:04, 3.65s/it] 56%|█████▌ | 290/520 [18:14<13:57, 3.64s/it] {'loss': 1.914, 'grad_norm': 0.0, 'learning_rate': 0.08632963916899268, 'epoch': 0.56} + 56%|█████▌ | 290/520 [18:14<13:57, 3.64s/it] 56%|█████▌ | 291/520 [18:17<13:53, 3.64s/it] {'loss': 2.0878, 'grad_norm': 0.0, 'learning_rate': 0.08571242871006202, 'epoch': 0.56} + 56%|█████▌ | 291/520 [18:17<13:53, 3.64s/it] 56%|█████▌ | 292/520 [18:21<13:48, 3.64s/it] {'loss': 2.1345, 'grad_norm': 0.0, 'learning_rate': 0.08509577338238256, 'epoch': 0.56} + 56%|█████▌ | 292/520 [18:21<13:48, 3.64s/it] 56%|█████▋ | 293/520 [18:24<13:44, 3.63s/it] {'loss': 2.1425, 'grad_norm': 0.0, 'learning_rate': 0.08447969714556484, 'epoch': 0.56} + 56%|█████▋ | 293/520 [18:24<13:44, 3.63s/it] 57%|█████▋ | 294/520 [18:28<13:43, 3.64s/it] {'loss': 2.2303, 'grad_norm': 0.0, 'learning_rate': 0.08386422393671933, 'epoch': 0.57} + 57%|█████▋ | 294/520 [18:28<13:43, 3.64s/it] 57%|█████▋ | 295/520 [18:32<13:47, 3.68s/it] {'loss': 1.832, 'grad_norm': 0.0, 'learning_rate': 0.08324937766952638, 'epoch': 0.57} + 57%|█████▋ | 295/520 [18:32<13:47, 3.68s/it] 57%|█████▋ | 296/520 [18:36<13:51, 3.71s/it] {'loss': 2.0787, 'grad_norm': 0.0, 'learning_rate': 0.08263518223330697, 'epoch': 0.57} + 57%|█████▋ | 296/520 [18:36<13:51, 3.71s/it] 57%|█████▋ | 297/520 [18:39<13:52, 3.73s/it] {'loss': 2.1771, 'grad_norm': 0.0, 'learning_rate': 0.08202166149209474, 'epoch': 0.57} + 57%|█████▋ | 297/520 [18:39<13:52, 3.73s/it] 57%|█████▋ | 298/520 [18:43<13:52, 3.75s/it] {'loss': 1.9442, 'grad_norm': 0.0, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:43<13:52, 3.75s/it] 57%|█████▊ | 299/520 [18:47<13:52, 3.77s/it] {'loss': 1.8794, 'grad_norm': 0.0, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:47<13:52, 3.77s/it] 58%|█████▊ | 300/520 [18:51<13:50, 3.77s/it] {'loss': 2.1153, 'grad_norm': 0.0, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:51<13:50, 3.77s/it] 58%|█████▊ | 301/520 [18:55<13:47, 3.78s/it] {'loss': 2.0707, 'grad_norm': 0.0, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:55<13:47, 3.78s/it] 58%|█████▊ | 302/520 [18:58<13:44, 3.78s/it] {'loss': 1.8532, 'grad_norm': 0.0, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:58<13:44, 3.78s/it] 58%|█████▊ | 303/520 [19:02<13:39, 3.78s/it] {'loss': 2.1844, 'grad_norm': 0.0, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [19:02<13:39, 3.78s/it] 58%|█████▊ | 304/520 [19:06<13:29, 3.75s/it] {'loss': 2.081, 'grad_norm': 0.0, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:06<13:29, 3.75s/it] 59%|█████▊ | 305/520 [19:10<13:19, 3.72s/it] {'loss': 2.1651, 'grad_norm': 0.0, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:10<13:19, 3.72s/it] 59%|█████▉ | 306/520 [19:13<13:24, 3.76s/it] {'loss': 2.1107, 'grad_norm': 0.0, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:13<13:24, 3.76s/it] 59%|█████▉ | 307/520 [19:18<13:54, 3.92s/it] {'loss': 2.0262, 'grad_norm': 0.0, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:18<13:54, 3.92s/it] 59%|█████▉ | 308/520 [19:22<13:47, 3.90s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:22<13:47, 3.90s/it] 59%|█████▉ | 309/520 [19:25<13:38, 3.88s/it] {'loss': 1.9269, 'grad_norm': 0.0, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:25<13:38, 3.88s/it] 60%|█████▉ | 310/520 [19:29<13:31, 3.86s/it] {'loss': 1.9954, 'grad_norm': 0.0, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:29<13:31, 3.86s/it] 60%|█████▉ | 311/520 [19:33<13:26, 3.86s/it] {'loss': 2.065, 'grad_norm': 0.0, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:33<13:26, 3.86s/it] 60%|██████ | 312/520 [19:37<13:21, 3.85s/it] {'loss': 2.1635, 'grad_norm': 0.0, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:37<13:21, 3.85s/it] 60%|██████ | 313/520 [19:41<13:17, 3.85s/it] {'loss': 1.8959, 'grad_norm': 0.0, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:41<13:17, 3.85s/it] 60%|██████ | 314/520 [19:45<13:40, 3.98s/it] {'loss': 2.0684, 'grad_norm': 0.0, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:45<13:40, 3.98s/it] 61%|██████ | 315/520 [19:49<13:19, 3.90s/it] {'loss': 2.0994, 'grad_norm': 0.0, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:49<13:19, 3.90s/it] 61%|██████ | 316/520 [19:53<13:30, 3.97s/it] {'loss': 2.1863, 'grad_norm': 0.0, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:53<13:30, 3.97s/it] 61%|██████ | 317/520 [19:57<13:07, 3.88s/it] {'loss': 1.9533, 'grad_norm': 0.0, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:57<13:07, 3.88s/it] 61%|██████ | 318/520 [20:00<12:48, 3.80s/it] {'loss': 2.2686, 'grad_norm': 0.0, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [20:00<12:48, 3.80s/it] 61%|██████▏ | 319/520 [20:04<12:57, 3.87s/it] {'loss': 1.8863, 'grad_norm': 0.0, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:04<12:57, 3.87s/it] 62%|██████▏ | 320/520 [20:08<12:38, 3.79s/it] {'loss': 2.0865, 'grad_norm': 0.0, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:08<12:38, 3.79s/it] 62%|██████▏ | 321/520 [20:11<12:26, 3.75s/it] {'loss': 2.0712, 'grad_norm': 0.0, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:11<12:26, 3.75s/it] 62%|██████▏ | 322/520 [20:15<12:16, 3.72s/it] {'loss': 1.8969, 'grad_norm': 0.0, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:15<12:16, 3.72s/it] 62%|██████▏ | 323/520 [20:19<12:07, 3.69s/it] {'loss': 2.0202, 'grad_norm': 0.0, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:19<12:07, 3.69s/it] 62%|██████▏ | 324/520 [20:22<12:01, 3.68s/it] {'loss': 2.0551, 'grad_norm': 0.0, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:22<12:01, 3.68s/it] 62%|██████▎ | 325/520 [20:26<11:55, 3.67s/it] {'loss': 2.1566, 'grad_norm': 0.0, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:26<11:55, 3.67s/it] 63%|██████▎ | 326/520 [20:30<11:49, 3.66s/it] {'loss': 2.1909, 'grad_norm': 0.0, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:30<11:49, 3.66s/it] 63%|██████▎ | 327/520 [20:33<11:44, 3.65s/it] {'loss': 2.061, 'grad_norm': 0.0, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:33<11:44, 3.65s/it] 63%|██████▎ | 328/520 [20:37<11:40, 3.65s/it] {'loss': 2.1111, 'grad_norm': 0.0, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:37<11:40, 3.65s/it] 63%|██████▎ | 329/520 [20:41<11:37, 3.65s/it] {'loss': 1.9436, 'grad_norm': 0.0, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:41<11:37, 3.65s/it] 63%|██████▎ | 330/520 [20:44<11:32, 3.64s/it] {'loss': 2.1281, 'grad_norm': 0.0, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:44<11:32, 3.64s/it] 64%|██████▎ | 331/520 [20:48<11:27, 3.64s/it] {'loss': 2.1551, 'grad_norm': 0.0, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:48<11:27, 3.64s/it] 64%|██████▍ | 332/520 [20:51<11:23, 3.64s/it] {'loss': 1.8491, 'grad_norm': 0.0, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:51<11:23, 3.64s/it] 64%|██████▍ | 333/520 [20:55<11:20, 3.64s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:55<11:20, 3.64s/it] 64%|██████▍ | 334/520 [20:59<11:17, 3.64s/it] {'loss': 2.1157, 'grad_norm': 0.0, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:59<11:17, 3.64s/it] 64%|██████▍ | 335/520 [21:02<11:14, 3.64s/it] {'loss': 2.013, 'grad_norm': 0.0, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:02<11:14, 3.64s/it] 65%|██████▍ | 336/520 [21:06<11:10, 3.64s/it] {'loss': 2.1874, 'grad_norm': 0.0, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:06<11:10, 3.64s/it] 65%|██████▍ | 337/520 [21:10<11:06, 3.64s/it] {'loss': 2.2477, 'grad_norm': 0.0, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:10<11:06, 3.64s/it] 65%|██████▌ | 338/520 [21:13<11:01, 3.64s/it] {'loss': 2.1774, 'grad_norm': 0.0, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:13<11:01, 3.64s/it] 65%|██████▌ | 339/520 [21:17<10:58, 3.64s/it] {'loss': 2.126, 'grad_norm': 0.0, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:17<10:58, 3.64s/it] 65%|██████▌ | 340/520 [21:21<10:55, 3.64s/it] {'loss': 2.0845, 'grad_norm': 0.0, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:21<10:55, 3.64s/it] 66%|██████▌ | 341/520 [21:24<10:50, 3.63s/it] {'loss': 2.094, 'grad_norm': 0.0, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:24<10:50, 3.63s/it] 66%|██████▌ | 342/520 [21:28<10:46, 3.63s/it] {'loss': 2.0199, 'grad_norm': 0.0, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:28<10:46, 3.63s/it] 66%|██████▌ | 343/520 [21:31<10:43, 3.64s/it] {'loss': 1.7182, 'grad_norm': 0.0, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:32<10:43, 3.64s/it] 66%|██████▌ | 344/520 [21:35<10:39, 3.64s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:35<10:39, 3.64s/it] 66%|██████▋ | 345/520 [21:39<10:36, 3.64s/it] {'loss': 2.2588, 'grad_norm': 0.0, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:39<10:36, 3.64s/it] 67%|██████▋ | 346/520 [21:42<10:34, 3.65s/it] {'loss': 1.859, 'grad_norm': 0.0, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:42<10:34, 3.65s/it] 67%|██████▋ | 347/520 [21:46<10:30, 3.65s/it] {'loss': 1.9277, 'grad_norm': 0.0, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:46<10:30, 3.65s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:50<10:27, 3.65s/it] {'loss': 2.405, 'grad_norm': 0.0, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:50<10:27, 3.65s/it] 67%|██████▋ | 349/520 [21:53<10:23, 3.65s/it] {'loss': 2.2236, 'grad_norm': 0.0, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:53<10:23, 3.65s/it] 67%|██████▋ | 350/520 [21:57<10:20, 3.65s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:57<10:20, 3.65s/it] 68%|██████▊ | 351/520 [22:01<10:16, 3.65s/it] {'loss': 2.0414, 'grad_norm': 0.0, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:01<10:16, 3.65s/it] 68%|██████▊ | 352/520 [22:04<10:14, 3.66s/it] {'loss': 2.0824, 'grad_norm': 0.0, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:04<10:14, 3.66s/it] 68%|██████▊ | 353/520 [22:08<10:12, 3.67s/it] {'loss': 1.8115, 'grad_norm': 0.0, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:08<10:12, 3.67s/it] 68%|██████▊ | 354/520 [22:12<10:07, 3.66s/it] {'loss': 1.8797, 'grad_norm': 0.0, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:12<10:07, 3.66s/it] 68%|██████▊ | 355/520 [22:15<10:04, 3.67s/it] {'loss': 2.0561, 'grad_norm': 0.0, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:15<10:04, 3.67s/it] 68%|██████▊ | 356/520 [22:19<10:01, 3.67s/it] {'loss': 2.2531, 'grad_norm': 0.0, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:19<10:01, 3.67s/it] 69%|██████▊ | 357/520 [22:23<09:57, 3.66s/it] {'loss': 2.0294, 'grad_norm': 0.0, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:23<09:57, 3.66s/it] 69%|██████▉ | 358/520 [22:26<09:52, 3.66s/it] {'loss': 2.0531, 'grad_norm': 0.0, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:26<09:52, 3.66s/it] 69%|██████▉ | 359/520 [22:30<09:48, 3.66s/it] {'loss': 2.011, 'grad_norm': 0.0, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:30<09:48, 3.66s/it] 69%|██████▉ | 360/520 [22:34<09:45, 3.66s/it] {'loss': 1.9948, 'grad_norm': 0.0, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:34<09:45, 3.66s/it] 69%|██████▉ | 361/520 [22:37<09:43, 3.67s/it] {'loss': 1.7504, 'grad_norm': 0.0, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:37<09:43, 3.67s/it] 70%|██████▉ | 362/520 [22:41<09:38, 3.66s/it] {'loss': 2.2058, 'grad_norm': 0.0, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:41<09:38, 3.66s/it] 70%|██████▉ | 363/520 [22:45<09:34, 3.66s/it] {'loss': 2.0753, 'grad_norm': 0.0, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:45<09:34, 3.66s/it] 70%|███████ | 364/520 [22:48<09:32, 3.67s/it] {'loss': 1.9811, 'grad_norm': 0.0, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:48<09:32, 3.67s/it] 70%|███████ | 365/520 [22:52<09:26, 3.65s/it] {'loss': 2.1137, 'grad_norm': 0.0, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:52<09:26, 3.65s/it] 70%|███████ | 366/520 [22:56<09:22, 3.65s/it] {'loss': 2.1027, 'grad_norm': 0.0, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:56<09:22, 3.65s/it] 71%|███████ | 367/520 [22:59<09:18, 3.65s/it] {'loss': 2.1701, 'grad_norm': 0.0, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:59<09:18, 3.65s/it] 71%|███████ | 368/520 [23:03<09:14, 3.65s/it] {'loss': 2.1175, 'grad_norm': 0.0, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:03<09:14, 3.65s/it] 71%|███████ | 369/520 [23:07<09:11, 3.65s/it] {'loss': 1.7789, 'grad_norm': 0.0, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:07<09:11, 3.65s/it] 71%|███████ | 370/520 [23:10<09:06, 3.64s/it] {'loss': 2.0015, 'grad_norm': 0.0, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:10<09:06, 3.64s/it] 71%|███████▏ | 371/520 [23:14<09:03, 3.64s/it] {'loss': 2.1704, 'grad_norm': 0.0, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:14<09:03, 3.64s/it] 72%|███████▏ | 372/520 [23:17<08:59, 3.64s/it] {'loss': 1.8294, 'grad_norm': 0.0, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:17<08:59, 3.64s/it] 72%|███████▏ | 373/520 [23:21<08:54, 3.64s/it] {'loss': 2.0181, 'grad_norm': 0.0, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:21<08:54, 3.64s/it] 72%|███████▏ | 374/520 [23:25<08:50, 3.63s/it] {'loss': 2.1018, 'grad_norm': 0.0, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:25<08:50, 3.63s/it] 72%|███████▏ | 375/520 [23:28<08:47, 3.64s/it] {'loss': 2.1132, 'grad_norm': 0.0, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:28<08:47, 3.64s/it] 72%|███████▏ | 376/520 [23:32<08:42, 3.63s/it] {'loss': 2.0573, 'grad_norm': 0.0, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:32<08:42, 3.63s/it] 72%|███████▎ | 377/520 [23:36<08:38, 3.63s/it] {'loss': 2.0899, 'grad_norm': 0.0, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:36<08:38, 3.63s/it] 73%|███████▎ | 378/520 [23:39<08:35, 3.63s/it] {'loss': 2.0289, 'grad_norm': 0.0, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:39<08:35, 3.63s/it] 73%|███████▎ | 379/520 [23:43<08:31, 3.63s/it] {'loss': 1.9774, 'grad_norm': 0.0, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:43<08:31, 3.63s/it] 73%|███████▎ | 380/520 [23:46<08:27, 3.63s/it] {'loss': 1.8319, 'grad_norm': 0.0, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:47<08:27, 3.63s/it] 73%|███████▎ | 381/520 [23:50<08:25, 3.64s/it] {'loss': 2.0371, 'grad_norm': 0.0, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:50<08:25, 3.64s/it] 73%|███████▎ | 382/520 [23:54<08:23, 3.65s/it] {'loss': 1.9153, 'grad_norm': 0.0, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:54<08:23, 3.65s/it] 74%|███████▎ | 383/520 [23:58<08:20, 3.65s/it] {'loss': 2.2443, 'grad_norm': 0.0, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:58<08:20, 3.65s/it] 74%|███████▍ | 384/520 [24:01<08:23, 3.70s/it] {'loss': 1.6572, 'grad_norm': 0.0, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:01<08:23, 3.70s/it] 74%|███████▍ | 385/520 [24:05<08:25, 3.74s/it] {'loss': 1.9484, 'grad_norm': 0.0, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:05<08:25, 3.74s/it] 74%|███████▍ | 386/520 [24:09<08:23, 3.76s/it] {'loss': 2.0001, 'grad_norm': 0.0, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:09<08:23, 3.76s/it] 74%|███████▍ | 387/520 [24:13<08:23, 3.78s/it] {'loss': 1.7967, 'grad_norm': 0.0, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:13<08:23, 3.78s/it] 75%|███████▍ | 388/520 [24:17<08:21, 3.80s/it] {'loss': 2.1252, 'grad_norm': 0.0, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:17<08:21, 3.80s/it] 75%|███████▍ | 389/520 [24:20<08:19, 3.82s/it] {'loss': 2.2819, 'grad_norm': 0.0, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:20<08:19, 3.82s/it] 75%|███████▌ | 390/520 [24:24<08:14, 3.80s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:24<08:14, 3.80s/it] 75%|███████▌ | 391/520 [24:28<08:04, 3.76s/it] {'loss': 2.0751, 'grad_norm': 0.0, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:28<08:04, 3.76s/it] 75%|███████▌ | 392/520 [24:32<07:55, 3.72s/it] {'loss': 2.0834, 'grad_norm': 0.0, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:32<07:55, 3.72s/it] 76%|███████▌ | 393/520 [24:35<07:48, 3.69s/it] {'loss': 1.6935, 'grad_norm': 0.0, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:35<07:48, 3.69s/it] 76%|███████▌ | 394/520 [24:39<07:41, 3.66s/it] {'loss': 2.1218, 'grad_norm': 0.0, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:39<07:41, 3.66s/it] 76%|███████▌ | 395/520 [24:42<07:36, 3.65s/it] {'loss': 2.1493, 'grad_norm': 0.0, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:42<07:36, 3.65s/it] 76%|███████▌ | 396/520 [24:46<07:31, 3.64s/it] {'loss': 2.0961, 'grad_norm': 0.0, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:46<07:31, 3.64s/it] 76%|███████▋ | 397/520 [24:50<07:28, 3.64s/it] {'loss': 2.0472, 'grad_norm': 0.0, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:50<07:28, 3.64s/it] 77%|███████▋ | 398/520 [24:53<07:23, 3.64s/it] {'loss': 2.2098, 'grad_norm': 0.0, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:53<07:23, 3.64s/it] 77%|███████▋ | 399/520 [24:57<07:20, 3.64s/it] {'loss': 1.8453, 'grad_norm': 0.0, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:57<07:20, 3.64s/it] 77%|███████▋ | 400/520 [25:01<07:17, 3.65s/it] {'loss': 1.8965, 'grad_norm': 0.0, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:01<07:17, 3.65s/it] 77%|███████▋ | 401/520 [25:04<07:14, 3.65s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:04<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:08<07:10, 3.65s/it] {'loss': 2.1351, 'grad_norm': 0.0, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:08<07:10, 3.65s/it] 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] {'loss': 2.1219, 'grad_norm': 0.0, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] 78%|███████▊ | 404/520 [25:15<07:03, 3.65s/it] {'loss': 2.2818, 'grad_norm': 0.0, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:15<07:03, 3.65s/it] 78%|███████▊ | 405/520 [25:19<06:59, 3.65s/it] {'loss': 1.875, 'grad_norm': 0.0, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:19<06:59, 3.65s/it] 78%|███████▊ | 406/520 [25:22<06:56, 3.66s/it] {'loss': 2.1806, 'grad_norm': 0.0, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:22<06:56, 3.66s/it] 78%|███████▊ | 407/520 [25:26<06:53, 3.66s/it] {'loss': 2.0986, 'grad_norm': 0.0, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:26<06:53, 3.66s/it] 78%|███████▊ | 408/520 [25:30<06:49, 3.65s/it] {'loss': 2.1517, 'grad_norm': 0.0, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:30<06:49, 3.65s/it] 79%|███████▊ | 409/520 [25:33<06:46, 3.66s/it] {'loss': 2.2385, 'grad_norm': 0.0, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:33<06:46, 3.66s/it] 79%|███████▉ | 410/520 [25:37<06:42, 3.66s/it] {'loss': 2.1727, 'grad_norm': 0.0, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:37<06:42, 3.66s/it] 79%|███████▉ | 411/520 [25:41<06:39, 3.66s/it] {'loss': 2.195, 'grad_norm': 0.0, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:41<06:39, 3.66s/it] 79%|███████▉ | 412/520 [25:45<06:38, 3.69s/it] {'loss': 2.0965, 'grad_norm': 0.0, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:45<06:38, 3.69s/it] 79%|███████▉ | 413/520 [25:48<06:34, 3.69s/it] {'loss': 1.916, 'grad_norm': 0.0, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:48<06:34, 3.69s/it] 80%|███████▉ | 414/520 [25:52<06:31, 3.69s/it] {'loss': 1.757, 'grad_norm': 0.0, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:52<06:31, 3.69s/it] 80%|███████▉ | 415/520 [25:56<06:27, 3.69s/it] {'loss': 2.0894, 'grad_norm': 0.0, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:56<06:27, 3.69s/it] 80%|████████ | 416/520 [25:59<06:23, 3.69s/it] {'loss': 2.3404, 'grad_norm': 0.0, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:59<06:23, 3.69s/it] 80%|████████ | 417/520 [26:03<06:19, 3.69s/it] {'loss': 2.0376, 'grad_norm': 0.0, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:03<06:19, 3.69s/it] 80%|████████ | 418/520 [26:07<06:16, 3.70s/it] {'loss': 1.9876, 'grad_norm': 0.0, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:07<06:16, 3.70s/it] 81%|████████ | 419/520 [26:10<06:12, 3.68s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:10<06:12, 3.68s/it] 81%|████████ | 420/520 [26:14<06:08, 3.69s/it] {'loss': 2.1783, 'grad_norm': 0.0, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:14<06:08, 3.69s/it] 81%|████████ | 421/520 [26:18<06:04, 3.68s/it] {'loss': 2.3788, 'grad_norm': 0.0, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:18<06:04, 3.68s/it] 81%|████████ | 422/520 [26:21<06:00, 3.68s/it] {'loss': 2.1751, 'grad_norm': 0.0, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:21<06:00, 3.68s/it] 81%|████████▏ | 423/520 [26:25<05:56, 3.67s/it] {'loss': 2.3239, 'grad_norm': 0.0, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:25<05:56, 3.67s/it] 82%|████████▏ | 424/520 [26:29<05:52, 3.68s/it] {'loss': 1.8431, 'grad_norm': 0.0, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:29<05:52, 3.68s/it] 82%|████████▏ | 425/520 [26:32<05:49, 3.68s/it] {'loss': 2.0338, 'grad_norm': 0.0, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:32<05:49, 3.68s/it] 82%|████████▏ | 426/520 [26:36<05:44, 3.67s/it] {'loss': 2.2809, 'grad_norm': 0.0, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:36<05:44, 3.67s/it] 82%|████████▏ | 427/520 [26:40<05:40, 3.66s/it] {'loss': 1.9615, 'grad_norm': 0.0, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:40<05:40, 3.66s/it] 82%|████████▏ | 428/520 [26:43<05:36, 3.66s/it] {'loss': 2.179, 'grad_norm': 0.0, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:43<05:36, 3.66s/it] 82%|████████▎ | 429/520 [26:47<05:33, 3.66s/it] {'loss': 2.1882, 'grad_norm': 0.0, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:47<05:33, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:51<05:29, 3.66s/it] {'loss': 2.0206, 'grad_norm': 0.0, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:51<05:29, 3.66s/it] 83%|████████▎ | 431/520 [26:54<05:25, 3.65s/it] {'loss': 1.8737, 'grad_norm': 0.0, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:54<05:25, 3.65s/it] 83%|████████▎ | 432/520 [26:58<05:21, 3.65s/it] {'loss': 2.0893, 'grad_norm': 0.0, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:58<05:21, 3.65s/it] 83%|████████▎ | 433/520 [27:02<05:19, 3.67s/it] {'loss': 2.1446, 'grad_norm': 0.0, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:02<05:19, 3.67s/it] 83%|████████▎ | 434/520 [27:05<05:16, 3.68s/it] {'loss': 2.162, 'grad_norm': 0.0, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:05<05:16, 3.68s/it] 84%|████████▎ | 435/520 [27:09<05:11, 3.66s/it] {'loss': 2.1714, 'grad_norm': 0.0, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:09<05:11, 3.66s/it] 84%|████████▍ | 436/520 [27:13<05:07, 3.66s/it] {'loss': 2.1073, 'grad_norm': 0.0, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:13<05:07, 3.66s/it] 84%|████████▍ | 437/520 [27:17<05:08, 3.71s/it] {'loss': 2.1399, 'grad_norm': 0.0, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:17<05:08, 3.71s/it] 84%|████████▍ | 438/520 [27:20<05:06, 3.74s/it] {'loss': 2.1034, 'grad_norm': 0.0, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:20<05:06, 3.74s/it] 84%|████████▍ | 439/520 [27:24<05:05, 3.77s/it] {'loss': 1.7664, 'grad_norm': 0.0, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:24<05:05, 3.77s/it] 85%|████████▍ | 440/520 [27:28<05:03, 3.79s/it] {'loss': 2.0058, 'grad_norm': 0.0, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:28<05:03, 3.79s/it] 85%|████████▍ | 441/520 [27:32<05:00, 3.81s/it] {'loss': 1.8248, 'grad_norm': 0.0, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:32<05:00, 3.81s/it] 85%|████████▌ | 442/520 [27:36<04:56, 3.81s/it] {'loss': 2.3179, 'grad_norm': 0.0, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:36<04:56, 3.81s/it] 85%|████████▌ | 443/520 [27:39<04:52, 3.80s/it] {'loss': 2.0141, 'grad_norm': 0.0, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:39<04:52, 3.80s/it] 85%|████████▌ | 444/520 [27:43<04:49, 3.81s/it] {'loss': 1.9937, 'grad_norm': 0.0, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:43<04:49, 3.81s/it] 86%|████████▌ | 445/520 [27:47<04:45, 3.81s/it] {'loss': 1.9637, 'grad_norm': 0.0, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:47<04:45, 3.81s/it] 86%|████████▌ | 446/520 [27:51<04:41, 3.81s/it] {'loss': 1.8401, 'grad_norm': 0.0, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:51<04:41, 3.81s/it] 86%|████████▌ | 447/520 [27:55<04:39, 3.82s/it] {'loss': 2.1475, 'grad_norm': 0.0, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:55<04:39, 3.82s/it] 86%|████████▌ | 448/520 [27:59<04:35, 3.82s/it] {'loss': 2.0884, 'grad_norm': 0.0, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:59<04:35, 3.82s/it] 86%|████████▋ | 449/520 [28:02<04:30, 3.81s/it] {'loss': 1.9783, 'grad_norm': 0.0, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:02<04:30, 3.81s/it] 87%|████████▋ | 450/520 [28:06<04:26, 3.81s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:06<04:26, 3.81s/it] 87%|████████▋ | 451/520 [28:10<04:23, 3.82s/it] {'loss': 2.1608, 'grad_norm': 0.0, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:10<04:23, 3.82s/it] 87%|████████▋ | 452/520 [28:14<04:19, 3.82s/it] {'loss': 1.8367, 'grad_norm': 0.0, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:14<04:19, 3.82s/it] 87%|████████▋ | 453/520 [28:18<04:16, 3.83s/it] {'loss': 1.9767, 'grad_norm': 0.0, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:18<04:16, 3.83s/it] 87%|████████▋ | 454/520 [28:21<04:12, 3.83s/it] {'loss': 2.0911, 'grad_norm': 0.0, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:21<04:12, 3.83s/it] 88%|████████▊ | 455/520 [28:25<04:08, 3.83s/it] {'loss': 2.0563, 'grad_norm': 0.0, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:25<04:08, 3.83s/it] 88%|████████▊ | 456/520 [28:29<04:04, 3.82s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:29<04:04, 3.82s/it] 88%|████████▊ | 457/520 [28:33<04:00, 3.82s/it] {'loss': 1.7164, 'grad_norm': 0.0, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:33<04:00, 3.82s/it] 88%|████████▊ | 458/520 [28:37<03:57, 3.83s/it] {'loss': 2.2316, 'grad_norm': 0.0, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:37<03:57, 3.83s/it] 88%|████████▊ | 459/520 [28:41<03:53, 3.83s/it] {'loss': 2.0823, 'grad_norm': 0.0, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:41<03:53, 3.83s/it] 88%|████████▊ | 460/520 [28:44<03:50, 3.84s/it] {'loss': 2.1604, 'grad_norm': 0.0, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:44<03:50, 3.84s/it] 89%|████████▊ | 461/520 [28:48<03:47, 3.86s/it] {'loss': 1.499, 'grad_norm': 0.0, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:48<03:47, 3.86s/it] 89%|████████▉ | 462/520 [28:52<03:43, 3.85s/it] {'loss': 1.9033, 'grad_norm': 0.0, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:52<03:43, 3.85s/it] 89%|████████▉ | 463/520 [28:56<03:39, 3.86s/it] {'loss': 2.3309, 'grad_norm': 0.0, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:56<03:39, 3.86s/it] 89%|████████▉ | 464/520 [29:00<03:35, 3.86s/it] {'loss': 2.1128, 'grad_norm': 0.0, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [29:00<03:35, 3.86s/it] 89%|████████▉ | 465/520 [29:04<03:32, 3.87s/it] {'loss': 2.1251, 'grad_norm': 0.0, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [29:04<03:32, 3.87s/it] 90%|████████▉ | 466/520 [29:08<03:28, 3.86s/it] {'loss': 1.9657, 'grad_norm': 0.0, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:08<03:28, 3.86s/it] 90%|████████▉ | 467/520 [29:12<03:24, 3.86s/it] {'loss': 1.8888, 'grad_norm': 0.0, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:12<03:24, 3.86s/it] 90%|█████████ | 468/520 [29:15<03:20, 3.85s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:15<03:20, 3.85s/it] 90%|█████████ | 469/520 [29:19<03:16, 3.84s/it] {'loss': 2.1097, 'grad_norm': 0.0, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:19<03:16, 3.84s/it] 90%|█████████ | 470/520 [29:23<03:12, 3.84s/it] {'loss': 2.0168, 'grad_norm': 0.0, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:23<03:12, 3.84s/it] 91%|█████████ | 471/520 [29:27<03:08, 3.85s/it] {'loss': 2.2295, 'grad_norm': 0.0, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:27<03:08, 3.85s/it] 91%|█████████ | 472/520 [29:31<03:04, 3.85s/it] {'loss': 2.1844, 'grad_norm': 0.0, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:31<03:04, 3.85s/it] 91%|█████████ | 473/520 [29:35<03:01, 3.85s/it] {'loss': 2.2132, 'grad_norm': 0.0, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:35<03:01, 3.85s/it] 91%|█████████ | 474/520 [29:38<02:56, 3.85s/it] {'loss': 1.9064, 'grad_norm': 0.0, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:38<02:56, 3.85s/it] 91%|█████████▏| 475/520 [29:42<02:53, 3.85s/it] {'loss': 1.8533, 'grad_norm': 0.0, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:42<02:53, 3.85s/it] 92%|█████████▏| 476/520 [29:46<02:49, 3.85s/it] {'loss': 2.165, 'grad_norm': 0.0, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:46<02:49, 3.85s/it] 92%|█████████▏| 477/520 [29:50<02:45, 3.85s/it] {'loss': 2.156, 'grad_norm': 0.0, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:50<02:45, 3.85s/it] 92%|█████████▏| 478/520 [29:54<02:41, 3.85s/it] {'loss': 2.069, 'grad_norm': 0.0, 'learning_rate': 0.00340741737109318, 'epoch': 0.92} + 92%|█████████▏| 478/520 [29:54<02:41, 3.85s/it] 92%|█████████▏| 479/520 [29:58<02:37, 3.84s/it] {'loss': 1.9471, 'grad_norm': 0.0, 'learning_rate': 0.003247964765680389, 'epoch': 0.92} + 92%|█████████▏| 479/520 [29:58<02:37, 3.84s/it] 92%|█████████▏| 480/520 [30:02<02:33, 3.84s/it] {'loss': 1.9655, 'grad_norm': 0.0, 'learning_rate': 0.0030922713770922153, 'epoch': 0.92} + 92%|█████████▏| 480/520 [30:02<02:33, 3.84s/it] 92%|█████████▎| 481/520 [30:05<02:30, 3.85s/it] {'loss': 1.8537, 'grad_norm': 0.0, 'learning_rate': 0.0029403432546609046, 'epoch': 0.93} + 92%|█████████▎| 481/520 [30:05<02:30, 3.85s/it] 93%|█████████▎| 482/520 [30:09<02:26, 3.85s/it] {'loss': 1.8943, 'grad_norm': 0.0, 'learning_rate': 0.0027921863014225504, 'epoch': 0.93} + 93%|█████████▎| 482/520 [30:09<02:26, 3.85s/it] 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2.1552, 'grad_norm': 0.0, 'learning_rate': 0.0019827512151456175, 'epoch': 0.94} + 94%|█████████▍| 488/520 [30:32<02:03, 3.86s/it] 94%|█████████▍| 489/520 [30:36<01:59, 3.86s/it] {'loss': 1.7671, 'grad_norm': 0.0, 'learning_rate': 0.0018611453956612345, 'epoch': 0.94} + 94%|█████████▍| 489/520 [30:36<01:59, 3.86s/it] 94%|█████████▍| 490/520 [30:40<01:55, 3.86s/it] {'loss': 2.1193, 'grad_norm': 0.0, 'learning_rate': 0.0017433526766711727, 'epoch': 0.94} + 94%|█████████▍| 490/520 [30:40<01:55, 3.86s/it] 94%|█████████▍| 491/520 [30:44<01:51, 3.85s/it] {'loss': 2.1849, 'grad_norm': 0.0, 'learning_rate': 0.0016293776349098677, 'epoch': 0.94} + 94%|█████████▍| 491/520 [30:44<01:51, 3.85s/it] 95%|█████████▍| 492/520 [30:48<01:47, 3.85s/it] {'loss': 2.1631, 'grad_norm': 0.0, 'learning_rate': 0.0015192246987791981, 'epoch': 0.95} + 95%|█████████▍| 492/520 [30:48<01:47, 3.85s/it] 95%|█████████▍| 493/520 [30:52<01:43, 3.85s/it] {'loss': 1.9782, 'grad_norm': 0.0, 'learning_rate': 0.0014128981481764114, 'epoch': 0.95} + 95%|█████████▍| 493/520 [30:52<01:43, 3.85s/it] 95%|█████████▌| 494/520 [30:56<01:40, 3.86s/it] {'loss': 2.001, 'grad_norm': 0.0, 'learning_rate': 0.0013104021143278911, 'epoch': 0.95} + 95%|█████████▌| 494/520 [30:56<01:40, 3.86s/it] 95%|█████████▌| 495/520 [30:59<01:36, 3.86s/it] {'loss': 2.0557, 'grad_norm': 0.0, 'learning_rate': 0.0012117405796285285, 'epoch': 0.95} + 95%|█████████▌| 495/520 [30:59<01:36, 3.86s/it] 95%|█████████▌| 496/520 [31:03<01:32, 3.87s/it] {'loss': 2.1532, 'grad_norm': 0.0, 'learning_rate': 0.0011169173774871477, 'epoch': 0.95} + 95%|█████████▌| 496/520 [31:03<01:32, 3.87s/it] 96%|█████████▌| 497/520 [31:07<01:29, 3.91s/it] {'loss': 1.8146, 'grad_norm': 0.0, 'learning_rate': 0.0010259361921774012, 'epoch': 0.96} + 96%|█████████▌| 497/520 [31:07<01:29, 3.91s/it] 96%|█████████▌| 498/520 [31:11<01:26, 3.92s/it] {'loss': 2.109, 'grad_norm': 0.0, 'learning_rate': 0.000938800558694719, 'epoch': 0.96} + 96%|█████████▌| 498/520 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'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:54<00:43, 3.91s/it] 98%|█████████▊| 510/520 [31:58<00:39, 3.90s/it] {'loss': 2.1279, 'grad_norm': 0.0, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:58<00:39, 3.90s/it] 98%|█████████▊| 511/520 [32:02<00:35, 3.91s/it] {'loss': 2.0474, 'grad_norm': 0.0, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [32:02<00:35, 3.91s/it] 98%|█████████▊| 512/520 [32:06<00:31, 3.91s/it] {'loss': 2.0024, 'grad_norm': 0.0, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [32:06<00:31, 3.91s/it] 99%|█████████▊| 513/520 [32:10<00:27, 3.91s/it] {'loss': 2.1995, 'grad_norm': 0.0, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [32:10<00:27, 3.91s/it] 99%|█████████▉| 514/520 [32:14<00:23, 3.91s/it] {'loss': 2.0174, 'grad_norm': 0.0, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 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100%|██████████| 520/520 [32:38<00:00, 4.15s/it] {'loss': 1.7443, 'grad_norm': 0.0, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:38<00:00, 4.15s/it] {'train_runtime': 1958.4772, 'train_samples_per_second': 33.97, 'train_steps_per_second': 0.266, 'train_loss': 2.066992656771953, 'epoch': 1.0} + 100%|██████████| 520/520 [32:38<00:00, 4.15s/it] 100%|██████████| 520/520 [32:38<00:00, 3.77s/it] +[2025-10-17 19:42:42,490] [INFO] [launch.py:348:main] Process 591719 exits successfully. +[2025-10-17 19:42:43,491] [INFO] [launch.py:348:main] Process 591717 exits successfully. +[2025-10-17 19:42:43,492] [INFO] [launch.py:348:main] Process 591715 exits successfully. +[2025-10-17 19:42:43,492] [INFO] [launch.py:348:main] Process 591714 exits successfully. +[2025-10-17 19:42:43,493] [INFO] [launch.py:348:main] Process 591720 exits successfully. +[2025-10-17 19:42:44,494] [INFO] [launch.py:348:main] Process 591716 exits successfully. +[2025-10-17 19:42:44,495] [INFO] [launch.py:348:main] Process 591718 exits successfully. +[2025-10-17 19:42:47,498] [INFO] [launch.py:348:main] Process 591713 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.1_2e-1_connector-7.0_1.1_2e-1_ablation_20251017_190835.log +Timestamp: 2025-10-17 19:42:49 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation_20251017_194249.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation_20251017_194249.log new file mode 100644 index 0000000000000000000000000000000000000000..5571bf46a74a05c4978dbe97727fee019c3a51ed --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation_20251017_194249.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation_20251017_194249.log +Timestamp: 2025-10-17 19:42:49 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:42:52,575] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:42:55,612] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 19:42:55,613] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 1.3 --temperature_mlp_text 1.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 1.3 --temperature_mlp_vision 1.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 1.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:42:58,162] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:42:59,207] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 19:42:59,207] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 19:42:59,207] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 19:42:59,207] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 19:42:59,207] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 19:42:59,207] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 19:42:59,207] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 19:42:59,209] [INFO] [launch.py:253:main] process 613605 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,211] [INFO] [launch.py:253:main] process 613606 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,213] [INFO] [launch.py:253:main] process 613607 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,215] [INFO] [launch.py:253:main] process 613608 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,217] [INFO] [launch.py:253:main] process 613609 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,219] [INFO] [launch.py:253:main] process 613610 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,220] [INFO] [launch.py:253:main] process 613611 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 19:42:59,222] [INFO] [launch.py:253:main] process 613612 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 19:43:05,823] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:05,944] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,159] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,188] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,190] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,232] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,240] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,311] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,326] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 19:43:06,355] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,566] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,589] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,589] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 19:43:06,590] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,645] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,709] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 19:43:06,720] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.3, 'temperature_mlp': 1.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.3, + "temperature_mlp": 1.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:613605:613605 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:613612:613612 [7] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:613611:613611 [6] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:613610:613610 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:613608:613608 [3] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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[0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 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04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 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: 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613607:615199 [2] NCCL INFO ncclCommInitRank comm 0x55ca50652b40 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613608:615197 [3] NCCL INFO ncclCommInitRank comm 0x55a936faff70 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613606:615194 [1] NCCL INFO ncclCommInitRank comm 0x55a19c3755c0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613605:615192 [0] NCCL INFO ncclCommInitRank comm 0x55e97c1e0c90 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:613612:615193 [7] NCCL INFO ncclCommInitRank comm 0x564f39de5c80 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613610:615196 [5] NCCL INFO ncclCommInitRank comm 0x560d7ca5b9e0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613609:615198 [4] NCCL INFO ncclCommInitRank comm 0x55d361284610 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +ywang29-vrdb-test1-worker-0:613611:615195 [6] NCCL INFO ncclCommInitRank comm 0x5591d660e4f0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xeeb5e2a9bc8af429 - Init COMPLETE +[2025-10-17 19:43:52,390] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 19:43:54,127] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 19:44:12,079 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 19:44:12,085 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters 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+language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:004->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read 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21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613605:620202 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613607:620209 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613612:620205 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613608:620208 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613610:620206 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613609:620207 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:613611:620204 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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ncclCommInitRank comm 0x7fcd8006b410 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xcb9a09c934cf638c - Init COMPLETE +ywang29-vrdb-test1-worker-0:613606:620203 [1] NCCL INFO ncclCommInitRank comm 0x7fefa006aed0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xcb9a09c934cf638c - Init COMPLETE + 0%| | 1/520 [00:14<2:05:15, 14.48s/it] {'loss': 2.0453, 'grad_norm': 0.0018613150557586597, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:05:15, 14.48s/it] 0%| | 2/520 [00:18<1:10:52, 8.21s/it] {'loss': 2.0549, 'grad_norm': 0.0020205899467666236, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:10:52, 8.21s/it] 1%| | 3/520 [00:22<53:33, 6.22s/it] {'loss': 2.1899, 'grad_norm': 0.002312244707831907, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<53:33, 6.22s/it] 1%| | 4/520 [00:25<45:22, 5.28s/it] {'loss': 2.0656, 'grad_norm': 0.0019110099770619001, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<45:22, 5.28s/it] 1%| | 5/520 [00:29<40:46, 4.75s/it] {'loss': 2.2333, 'grad_norm': 0.002110279565060103, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<40:46, 4.75s/it] 1%| | 6/520 [00:33<38:01, 4.44s/it] {'loss': 1.6754, 'grad_norm': 0.0010790405007743901, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<38:01, 4.44s/it] 1%|▏ | 7/520 [00:37<36:12, 4.23s/it] {'loss': 2.0776, 'grad_norm': 0.002084801878884547, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:37<36:12, 4.23s/it] 2%|▏ | 8/520 [00:41<36:44, 4.31s/it] {'loss': 2.0541, 'grad_norm': 0.0017603593063738327, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:41<36:44, 4.31s/it] 2%|▏ | 9/520 [00:46<36:46, 4.32s/it] {'loss': 1.8177, 'grad_norm': 0.001194204752095848, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:46<36:46, 4.32s/it] 2%|▏ | 10/520 [00:50<35:25, 4.17s/it] {'loss': 1.6193, 'grad_norm': 0.0012343956317687746, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 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If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:02<34:51, 4.13s/it] {'loss': 1.5646, 'grad_norm': 0.0005170959265468085, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:02<34:51, 4.13s/it] 3%|▎ | 14/520 [01:06<33:56, 4.02s/it] {'loss': 1.5765, 'grad_norm': 0.00045586356772064907, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:06<33:56, 4.02s/it] 3%|▎ | 15/520 [01:09<33:15, 3.95s/it] {'loss': 1.4566, 'grad_norm': 0.0002788683243885835, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:09<33:15, 3.95s/it] 3%|▎ | 16/520 [01:13<32:41, 3.89s/it] {'loss': 1.4133, 'grad_norm': 0.00037943078777597384, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:13<32:41, 3.89s/it] 3%|▎ | 17/520 [01:17<32:21, 3.86s/it] {'loss': 1.5733, 'grad_norm': 0.0004403517261616132, 'learning_rate': 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[18:59<13:24, 3.80s/it] {'loss': 1.2846, 'grad_norm': 0.0005615450903052099, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [18:59<13:24, 3.80s/it] 59%|█████▉ | 309/520 [19:03<13:21, 3.80s/it] {'loss': 1.1792, 'grad_norm': 0.0005727970417181587, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:03<13:21, 3.80s/it] 60%|█████▉ | 310/520 [19:07<13:17, 3.80s/it] {'loss': 1.1554, 'grad_norm': 0.0005885185017500511, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:07<13:17, 3.80s/it] 60%|█████▉ | 311/520 [19:11<13:12, 3.79s/it] {'loss': 1.1435, 'grad_norm': 0.0005687829408722916, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:11<13:12, 3.79s/it] 60%|██████ | 312/520 [19:14<12:59, 3.75s/it] {'loss': 1.1311, 'grad_norm': 0.000612988004167634, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:14<12:59, 3.75s/it] 60%|██████ | 313/520 [19:18<12:47, 3.71s/it] {'loss': 1.1034, 'grad_norm': 0.0005277418684261891, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:18<12:47, 3.71s/it] 60%|██████ | 314/520 [19:22<13:06, 3.82s/it] {'loss': 1.1471, 'grad_norm': 0.0005691899361845276, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:22<13:06, 3.82s/it] 61%|██████ | 315/520 [19:25<12:49, 3.75s/it] {'loss': 1.1852, 'grad_norm': 0.0006207665385621791, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:25<12:49, 3.75s/it] 61%|██████ | 316/520 [19:29<13:01, 3.83s/it] {'loss': 1.1355, 'grad_norm': 0.0006076979045426888, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:29<13:01, 3.83s/it] 61%|██████ | 317/520 [19:33<12:46, 3.78s/it] {'loss': 1.1345, 'grad_norm': 0.0005202411022554888, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:33<12:46, 3.78s/it] 61%|██████ | 318/520 [19:37<12:34, 3.73s/it] {'loss': 1.2447, 'grad_norm': 0.0006120504397269591, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:37<12:34, 3.73s/it] 61%|██████▏ | 319/520 [19:41<12:42, 3.79s/it] {'loss': 1.1307, 'grad_norm': 0.0005295762205702037, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:41<12:42, 3.79s/it] 62%|██████▏ | 320/520 [19:44<12:27, 3.74s/it] {'loss': 1.071, 'grad_norm': 0.0005702049351714945, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:44<12:27, 3.74s/it] 62%|██████▏ | 321/520 [19:48<12:18, 3.71s/it] {'loss': 1.2672, 'grad_norm': 0.0005738457618239797, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [19:48<12:18, 3.71s/it] 62%|██████▏ | 322/520 [19:52<12:11, 3.70s/it] {'loss': 1.0861, 'grad_norm': 0.0005584781665801845, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [19:52<12:11, 3.70s/it] 62%|██████▏ | 323/520 [19:55<12:05, 3.68s/it] {'loss': 1.1598, 'grad_norm': 0.0005742085795614633, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [19:55<12:05, 3.68s/it] 62%|██████▏ | 324/520 [19:59<12:02, 3.69s/it] {'loss': 1.2112, 'grad_norm': 0.0005887074314251591, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [19:59<12:02, 3.69s/it] 62%|██████▎ | 325/520 [20:03<11:56, 3.68s/it] {'loss': 1.2093, 'grad_norm': 0.0006059132199025651, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:03<11:56, 3.68s/it] 63%|██████▎ | 326/520 [20:06<11:51, 3.67s/it] {'loss': 1.2101, 'grad_norm': 0.0006222821885527819, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:06<11:51, 3.67s/it] 63%|██████▎ | 327/520 [20:10<11:46, 3.66s/it] {'loss': 1.1875, 'grad_norm': 0.0006001605625190354, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:10<11:46, 3.66s/it] 63%|██████▎ | 328/520 [20:14<11:41, 3.65s/it] {'loss': 1.2477, 'grad_norm': 0.000603249595798849, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:14<11:41, 3.65s/it] 63%|██████▎ | 329/520 [20:17<11:39, 3.66s/it] {'loss': 1.1314, 'grad_norm': 0.0005208927540614367, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:17<11:39, 3.66s/it] 63%|██████▎ | 330/520 [20:21<11:34, 3.66s/it] {'loss': 1.2086, 'grad_norm': 0.0005491685542981223, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:21<11:34, 3.66s/it] 64%|██████▎ | 331/520 [20:25<11:30, 3.65s/it] {'loss': 1.1679, 'grad_norm': 0.000614065674874423, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:25<11:30, 3.65s/it] 64%|██████▍ | 332/520 [20:28<11:29, 3.67s/it] {'loss': 1.2162, 'grad_norm': 0.0005268774014683341, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:28<11:29, 3.67s/it] 64%|██████▍ | 333/520 [20:32<11:24, 3.66s/it] {'loss': 1.3009, 'grad_norm': 0.0006223825737009473, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:32<11:24, 3.66s/it] 64%|██████▍ | 334/520 [20:35<11:19, 3.65s/it] {'loss': 1.212, 'grad_norm': 0.0006241799289799804, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:36<11:19, 3.65s/it] 64%|██████▍ | 335/520 [20:39<11:13, 3.64s/it] {'loss': 1.2123, 'grad_norm': 0.000568220567908215, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:39<11:13, 3.64s/it] 65%|██████▍ | 336/520 [20:43<11:08, 3.63s/it] {'loss': 1.1227, 'grad_norm': 0.0006326008721490056, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:43<11:08, 3.63s/it] 65%|██████▍ | 337/520 [20:46<11:03, 3.63s/it] {'loss': 1.1136, 'grad_norm': 0.0005942174771581469, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:46<11:03, 3.63s/it] 65%|██████▌ | 338/520 [20:50<10:59, 3.62s/it] {'loss': 1.2195, 'grad_norm': 0.0005864597622357373, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [20:50<10:59, 3.62s/it] 65%|██████▌ | 339/520 [20:54<10:55, 3.62s/it] {'loss': 1.1612, 'grad_norm': 0.0006262677737073471, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [20:54<10:55, 3.62s/it] 65%|██████▌ | 340/520 [20:57<10:54, 3.63s/it] {'loss': 1.1494, 'grad_norm': 0.00056807367704965, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [20:57<10:54, 3.63s/it] 66%|██████▌ | 341/520 [21:01<10:50, 3.64s/it] {'loss': 1.1827, 'grad_norm': 0.0006203378741739676, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:01<10:50, 3.64s/it] 66%|██████▌ | 342/520 [21:05<10:50, 3.65s/it] {'loss': 1.1945, 'grad_norm': 0.0006707992389337316, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:05<10:50, 3.65s/it] 66%|██████▌ | 343/520 [21:08<10:45, 3.65s/it] {'loss': 1.1431, 'grad_norm': 0.00047758934640654526, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:08<10:45, 3.65s/it] 66%|██████▌ | 344/520 [21:12<10:41, 3.65s/it] {'loss': 1.1385, 'grad_norm': 0.0005372340065746955, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:12<10:41, 3.65s/it] 66%|██████▋ | 345/520 [21:15<10:37, 3.64s/it] {'loss': 1.2359, 'grad_norm': 0.0006056073248193058, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:15<10:37, 3.64s/it] 67%|██████▋ | 346/520 [21:19<10:36, 3.66s/it] {'loss': 1.1612, 'grad_norm': 0.0005575587141307445, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:19<10:36, 3.66s/it] 67%|██████▋ | 347/520 [21:23<10:30, 3.65s/it] {'loss': 1.1564, 'grad_norm': 0.0005380997971068, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:23<10:30, 3.65s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:26<10:24, 3.63s/it] {'loss': 1.1141, 'grad_norm': 0.0006863861711559973, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:26<10:24, 3.63s/it] 67%|██████▋ | 349/520 [21:30<10:20, 3.63s/it] {'loss': 1.1457, 'grad_norm': 0.0005916130573293291, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:30<10:20, 3.63s/it] 67%|██████▋ | 350/520 [21:34<10:16, 3.62s/it] {'loss': 1.1918, 'grad_norm': 0.0006032804996273807, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:34<10:16, 3.62s/it] 68%|██████▊ | 351/520 [21:37<10:13, 3.63s/it] {'loss': 1.1033, 'grad_norm': 0.0005503406759360848, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:37<10:13, 3.63s/it] 68%|██████▊ | 352/520 [21:41<10:10, 3.63s/it] {'loss': 1.2162, 'grad_norm': 0.0005449571399570722, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:41<10:10, 3.63s/it] 68%|██████▊ | 353/520 [21:45<10:08, 3.64s/it] {'loss': 1.1397, 'grad_norm': 0.00047965548936438083, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:45<10:08, 3.64s/it] 68%|██████▊ | 354/520 [21:48<10:11, 3.68s/it] {'loss': 1.2276, 'grad_norm': 0.0005258727575611176, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:48<10:11, 3.68s/it] 68%|██████▊ | 355/520 [21:52<10:13, 3.72s/it] {'loss': 1.1668, 'grad_norm': 0.0005847970089533888, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [21:52<10:13, 3.72s/it] 68%|██████▊ | 356/520 [21:56<10:18, 3.77s/it] {'loss': 1.1666, 'grad_norm': 0.0005994221260060033, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [21:56<10:18, 3.77s/it] 69%|██████▊ | 357/520 [22:00<10:15, 3.78s/it] {'loss': 1.2025, 'grad_norm': 0.0005570694064313466, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:00<10:15, 3.78s/it] 69%|██████▉ | 358/520 [22:04<10:14, 3.79s/it] {'loss': 1.1306, 'grad_norm': 0.0005922021359811954, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:04<10:14, 3.79s/it] 69%|██████▉ | 359/520 [22:08<10:12, 3.81s/it] {'loss': 1.1721, 'grad_norm': 0.000575327031723696, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:08<10:12, 3.81s/it] 69%|██████▉ | 360/520 [22:11<10:08, 3.80s/it] {'loss': 1.1766, 'grad_norm': 0.000560460694341142, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:11<10:08, 3.80s/it] 69%|██████▉ | 361/520 [22:15<10:05, 3.81s/it] {'loss': 1.1975, 'grad_norm': 0.0005102747145826079, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:15<10:05, 3.81s/it] 70%|██████▉ | 362/520 [22:19<10:03, 3.82s/it] {'loss': 1.1767, 'grad_norm': 0.0006393660012135698, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:19<10:03, 3.82s/it] 70%|██████▉ | 363/520 [22:23<09:58, 3.81s/it] {'loss': 1.2079, 'grad_norm': 0.0005795073373387437, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:23<09:58, 3.81s/it] 70%|███████ | 364/520 [22:27<09:56, 3.82s/it] {'loss': 1.2092, 'grad_norm': 0.0005687652407984354, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:27<09:56, 3.82s/it] 70%|███████ | 365/520 [22:30<09:52, 3.82s/it] {'loss': 1.2568, 'grad_norm': 0.0005941805503906923, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:30<09:52, 3.82s/it] 70%|███████ | 366/520 [22:34<09:48, 3.82s/it] {'loss': 1.2272, 'grad_norm': 0.0005630575832446503, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:34<09:48, 3.82s/it] 71%|███████ | 367/520 [22:38<09:46, 3.83s/it] {'loss': 1.2256, 'grad_norm': 0.0006120427601407615, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:38<09:46, 3.83s/it] 71%|███████ | 368/520 [22:42<09:38, 3.81s/it] {'loss': 1.0775, 'grad_norm': 0.0005926556295714574, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:42<09:38, 3.81s/it] 71%|███████ | 369/520 [22:45<09:26, 3.75s/it] {'loss': 1.1745, 'grad_norm': 0.0005218876419898592, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:45<09:26, 3.75s/it] 71%|███████ | 370/520 [22:49<09:15, 3.71s/it] {'loss': 1.1378, 'grad_norm': 0.0005518600732719374, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:49<09:15, 3.71s/it] 71%|███████▏ | 371/520 [22:53<09:07, 3.68s/it] {'loss': 1.1258, 'grad_norm': 0.0006153840668603023, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [22:53<09:07, 3.68s/it] 72%|███████▏ | 372/520 [22:56<09:00, 3.65s/it] {'loss': 1.2386, 'grad_norm': 0.0005081700941810533, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [22:56<09:00, 3.65s/it] 72%|███████▏ | 373/520 [23:00<08:54, 3.63s/it] {'loss': 1.1284, 'grad_norm': 0.0006103845108926129, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:00<08:54, 3.63s/it] 72%|███████▏ | 374/520 [23:03<08:48, 3.62s/it] {'loss': 1.223, 'grad_norm': 0.0006084880373017822, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:03<08:48, 3.62s/it] 72%|███████▏ | 375/520 [23:07<08:44, 3.61s/it] {'loss': 1.139, 'grad_norm': 0.000605694758013702, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:07<08:44, 3.61s/it] 72%|███████▏ | 376/520 [23:11<08:38, 3.60s/it] {'loss': 1.2436, 'grad_norm': 0.0005620802963834305, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:11<08:38, 3.60s/it] 72%|███████▎ | 377/520 [23:14<08:34, 3.60s/it] {'loss': 1.1727, 'grad_norm': 0.0006200240354263453, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:14<08:34, 3.60s/it] 73%|███████▎ | 378/520 [23:18<08:30, 3.60s/it] {'loss': 1.2381, 'grad_norm': 0.0005488180657700771, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:18<08:30, 3.60s/it] 73%|███████▎ | 379/520 [23:21<08:26, 3.60s/it] {'loss': 1.203, 'grad_norm': 0.0005429193360348635, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:21<08:26, 3.60s/it] 73%|███████▎ | 380/520 [23:25<08:22, 3.59s/it] {'loss': 1.2179, 'grad_norm': 0.000583117103729078, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:25<08:22, 3.59s/it] 73%|███████▎ | 381/520 [23:29<08:20, 3.60s/it] {'loss': 1.2166, 'grad_norm': 0.0005521537662568052, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:29<08:20, 3.60s/it] 73%|███████▎ | 382/520 [23:32<08:16, 3.60s/it] {'loss': 1.1896, 'grad_norm': 0.0005274490831537029, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:32<08:16, 3.60s/it] 74%|███████▎ | 383/520 [23:36<08:14, 3.61s/it] {'loss': 1.0598, 'grad_norm': 0.0006335298659270469, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:36<08:14, 3.61s/it] 74%|███████▍ | 384/520 [23:39<08:12, 3.62s/it] {'loss': 1.2083, 'grad_norm': 0.000490506039410536, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:39<08:12, 3.62s/it] 74%|███████▍ | 385/520 [23:43<08:08, 3.62s/it] {'loss': 1.2029, 'grad_norm': 0.0005407375039531775, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:43<08:08, 3.62s/it] 74%|███████▍ | 386/520 [23:47<08:06, 3.63s/it] {'loss': 1.1518, 'grad_norm': 0.0004964453267010092, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:47<08:06, 3.63s/it] 74%|███████▍ | 387/520 [23:50<08:01, 3.62s/it] {'loss': 1.2362, 'grad_norm': 0.0005520748803669171, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [23:50<08:01, 3.62s/it] 75%|███████▍ | 388/520 [23:54<07:58, 3.62s/it] {'loss': 1.1131, 'grad_norm': 0.0005574221132527857, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [23:54<07:58, 3.62s/it] 75%|███████▍ | 389/520 [23:58<07:54, 3.62s/it] {'loss': 1.16, 'grad_norm': 0.0007125458811911933, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [23:58<07:54, 3.62s/it] 75%|███████▌ | 390/520 [24:01<07:50, 3.62s/it] {'loss': 1.2298, 'grad_norm': 0.0005607213699227752, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:01<07:50, 3.62s/it] 75%|███████▌ | 391/520 [24:05<07:47, 3.62s/it] {'loss': 1.2869, 'grad_norm': 0.0005840490977825528, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:05<07:47, 3.62s/it] 75%|███████▌ | 392/520 [24:08<07:44, 3.63s/it] {'loss': 1.1129, 'grad_norm': 0.0005639427598662196, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:08<07:44, 3.63s/it] 76%|███████▌ | 393/520 [24:12<07:40, 3.63s/it] {'loss': 1.101, 'grad_norm': 0.0004901794156500055, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:12<07:40, 3.63s/it] 76%|███████▌ | 394/520 [24:16<07:36, 3.62s/it] {'loss': 1.1845, 'grad_norm': 0.0006085721542819138, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:16<07:36, 3.62s/it] 76%|███████▌ | 395/520 [24:19<07:32, 3.62s/it] {'loss': 1.1505, 'grad_norm': 0.0006245696327996705, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:19<07:32, 3.62s/it] 76%|███████▌ | 396/520 [24:23<07:28, 3.62s/it] {'loss': 1.225, 'grad_norm': 0.0006169597501310446, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:23<07:28, 3.62s/it] 76%|███████▋ | 397/520 [24:27<07:24, 3.62s/it] {'loss': 1.2, 'grad_norm': 0.0005644024231175987, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:27<07:24, 3.62s/it] 77%|███████▋ | 398/520 [24:30<07:20, 3.61s/it] {'loss': 1.1945, 'grad_norm': 0.0006166565858580621, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:30<07:20, 3.61s/it] 77%|███████▋ | 399/520 [24:34<07:19, 3.63s/it] {'loss': 1.1303, 'grad_norm': 0.0005564103339531358, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:34<07:19, 3.63s/it] 77%|███████▋ | 400/520 [24:37<07:14, 3.62s/it] {'loss': 1.1638, 'grad_norm': 0.0005248204106713134, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:37<07:14, 3.62s/it] 77%|███████▋ | 401/520 [24:41<07:10, 3.62s/it] {'loss': 1.0376, 'grad_norm': 0.0006271321057546111, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:41<07:10, 3.62s/it] 77%|███████▋ | 402/520 [24:45<07:06, 3.61s/it] {'loss': 1.1676, 'grad_norm': 0.0005983149984282459, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:45<07:06, 3.61s/it] 78%|███████▊ | 403/520 [24:48<07:02, 3.61s/it] {'loss': 1.1884, 'grad_norm': 0.0006303007867907392, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [24:48<07:02, 3.61s/it] 78%|███████▊ | 404/520 [24:52<06:59, 3.61s/it] {'loss': 1.1004, 'grad_norm': 0.0006717495207994833, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [24:52<06:59, 3.61s/it] 78%|███████▊ | 405/520 [24:55<06:55, 3.61s/it] {'loss': 1.1475, 'grad_norm': 0.0005880783235655804, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [24:55<06:55, 3.61s/it] 78%|███████▊ | 406/520 [24:59<06:51, 3.61s/it] {'loss': 1.073, 'grad_norm': 0.0006726423045988746, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [24:59<06:51, 3.61s/it] 78%|███████▊ | 407/520 [25:03<06:47, 3.61s/it] {'loss': 1.2656, 'grad_norm': 0.0005921282044992097, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:03<06:47, 3.61s/it] 78%|███████▊ | 408/520 [25:06<06:44, 3.61s/it] {'loss': 1.182, 'grad_norm': 0.0006415708176740499, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:06<06:44, 3.61s/it] 79%|███████▊ | 409/520 [25:10<06:39, 3.60s/it] {'loss': 1.298, 'grad_norm': 0.000616747312856979, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:10<06:39, 3.60s/it] 79%|███████▉ | 410/520 [25:14<06:36, 3.60s/it] {'loss': 1.0387, 'grad_norm': 0.0005861089510998372, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:14<06:36, 3.60s/it] 79%|███████▉ | 411/520 [25:17<06:32, 3.60s/it] {'loss': 1.277, 'grad_norm': 0.0006233022679548113, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:17<06:32, 3.60s/it] 79%|███████▉ | 412/520 [25:21<06:31, 3.62s/it] {'loss': 1.1861, 'grad_norm': 0.0005708539481435398, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:21<06:31, 3.62s/it] 79%|███████▉ | 413/520 [25:25<06:35, 3.69s/it] {'loss': 1.1635, 'grad_norm': 0.0005402897723585585, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:25<06:35, 3.69s/it] 80%|███████▉ | 414/520 [25:28<06:31, 3.69s/it] {'loss': 0.974, 'grad_norm': 0.000474937084940119, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:28<06:31, 3.69s/it] 80%|███████▉ | 415/520 [25:32<06:25, 3.67s/it] {'loss': 1.1694, 'grad_norm': 0.0005519541367411089, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:32<06:25, 3.67s/it] 80%|████████ | 416/520 [25:36<06:19, 3.65s/it] {'loss': 1.0724, 'grad_norm': 0.0006334337874186144, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:36<06:19, 3.65s/it] 80%|████████ | 417/520 [25:39<06:14, 3.63s/it] {'loss': 1.2347, 'grad_norm': 0.0005772746181832312, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:39<06:14, 3.63s/it] 80%|████████ | 418/520 [25:43<06:09, 3.62s/it] {'loss': 1.2313, 'grad_norm': 0.0005440094574868929, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:43<06:09, 3.62s/it] 81%|████████ | 419/520 [25:46<06:04, 3.61s/it] {'loss': 1.2246, 'grad_norm': 0.0006396043743424426, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:46<06:04, 3.61s/it] 81%|████████ | 420/520 [25:50<06:00, 3.61s/it] {'loss': 1.1149, 'grad_norm': 0.0006027477010868047, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [25:50<06:00, 3.61s/it] 81%|████████ | 421/520 [25:54<05:56, 3.60s/it] {'loss': 1.0516, 'grad_norm': 0.0006080559114205797, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [25:54<05:56, 3.60s/it] 81%|████████ | 422/520 [25:57<05:53, 3.60s/it] {'loss': 1.175, 'grad_norm': 0.0006192274621030219, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [25:57<05:53, 3.60s/it] 81%|████████▏ | 423/520 [26:01<05:49, 3.60s/it] {'loss': 1.1418, 'grad_norm': 0.0006231917996131318, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:01<05:49, 3.60s/it] 82%|████████▏ | 424/520 [26:04<05:47, 3.62s/it] {'loss': 1.2457, 'grad_norm': 0.0005313409002315597, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:04<05:47, 3.62s/it] 82%|████████▏ | 425/520 [26:08<05:43, 3.62s/it] {'loss': 1.1576, 'grad_norm': 0.0005803628096163002, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:08<05:43, 3.62s/it] 82%|████████▏ | 426/520 [26:12<05:39, 3.61s/it] {'loss': 1.1947, 'grad_norm': 0.0007487447099925833, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:12<05:39, 3.61s/it] 82%|████████▏ | 427/520 [26:15<05:36, 3.62s/it] {'loss': 1.0904, 'grad_norm': 0.0005596854967561602, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:15<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:19<05:32, 3.62s/it] {'loss': 1.0863, 'grad_norm': 0.0006378609106511806, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:19<05:32, 3.62s/it] 82%|████████▎ | 429/520 [26:22<05:29, 3.62s/it] {'loss': 1.1855, 'grad_norm': 0.0005926841531739858, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:22<05:29, 3.62s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:26<05:25, 3.62s/it] {'loss': 1.1843, 'grad_norm': 0.000557258711784688, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:26<05:25, 3.62s/it] 83%|████████▎ | 431/520 [26:30<05:22, 3.62s/it] {'loss': 1.137, 'grad_norm': 0.0005575847025472441, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:30<05:22, 3.62s/it] 83%|████████▎ | 432/520 [26:33<05:18, 3.62s/it] {'loss': 1.089, 'grad_norm': 0.0006045752759625362, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:33<05:18, 3.62s/it] 83%|████████▎ | 433/520 [26:37<05:14, 3.61s/it] {'loss': 1.2237, 'grad_norm': 0.0005773702125771982, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:37<05:14, 3.61s/it] 83%|████████▎ | 434/520 [26:41<05:11, 3.62s/it] {'loss': 0.9767, 'grad_norm': 0.0006075241473346136, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:41<05:11, 3.62s/it] 84%|████████▎ | 435/520 [26:44<05:07, 3.61s/it] {'loss': 1.2542, 'grad_norm': 0.000626835308784682, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:44<05:07, 3.61s/it] 84%|████████▍ | 436/520 [26:48<05:03, 3.61s/it] {'loss': 1.0658, 'grad_norm': 0.0006195491974283951, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:48<05:03, 3.61s/it] 84%|████████▍ | 437/520 [26:51<05:01, 3.63s/it] {'loss': 1.277, 'grad_norm': 0.0005985227229842803, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [26:51<05:01, 3.63s/it] 84%|████████▍ | 438/520 [26:55<04:57, 3.63s/it] {'loss': 1.0967, 'grad_norm': 0.0005851688906147377, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [26:55<04:57, 3.63s/it] 84%|████████▍ | 439/520 [26:59<04:53, 3.62s/it] {'loss': 1.1203, 'grad_norm': 0.00047066746698962766, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [26:59<04:53, 3.62s/it] 85%|████████▍ | 440/520 [27:02<04:49, 3.62s/it] {'loss': 1.1335, 'grad_norm': 0.0005968622657324923, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:02<04:49, 3.62s/it] 85%|████████▍ | 441/520 [27:06<04:46, 3.63s/it] {'loss': 1.1326, 'grad_norm': 0.0005602419956480236, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:06<04:46, 3.63s/it] 85%|████████▌ | 442/520 [27:10<04:45, 3.66s/it] {'loss': 1.1953, 'grad_norm': 0.0006485124130875178, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:10<04:45, 3.66s/it] 85%|████████▌ | 443/520 [27:13<04:45, 3.71s/it] {'loss': 1.2017, 'grad_norm': 0.0005677431251429884, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:13<04:45, 3.71s/it] 85%|████████▌ | 444/520 [27:17<04:46, 3.77s/it] {'loss': 1.1712, 'grad_norm': 0.000520614355930228, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:17<04:46, 3.77s/it] 86%|████████▌ | 445/520 [27:21<04:43, 3.78s/it] {'loss': 1.0994, 'grad_norm': 0.0005554976674307937, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:21<04:43, 3.78s/it] 86%|████████▌ | 446/520 [27:25<04:41, 3.80s/it] {'loss': 1.2088, 'grad_norm': 0.0005119470351400567, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:25<04:41, 3.80s/it] 86%|████████▌ | 447/520 [27:29<04:38, 3.82s/it] {'loss': 1.1661, 'grad_norm': 0.0005674220947934531, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:29<04:38, 3.82s/it] 86%|████████▌ | 448/520 [27:33<04:34, 3.81s/it] {'loss': 1.1665, 'grad_norm': 0.0006395682121362094, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:33<04:34, 3.81s/it] 86%|████████▋ | 449/520 [27:36<04:26, 3.75s/it] {'loss': 1.1703, 'grad_norm': 0.0005665427074936564, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:36<04:26, 3.75s/it] 87%|████████▋ | 450/520 [27:40<04:19, 3.71s/it] {'loss': 1.1929, 'grad_norm': 0.0005981779475301226, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:40<04:19, 3.71s/it] 87%|████████▋ | 451/520 [27:44<04:14, 3.69s/it] {'loss': 1.198, 'grad_norm': 0.000611103682343404, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:44<04:14, 3.69s/it] 87%|████████▋ | 452/520 [27:47<04:09, 3.67s/it] {'loss': 1.2167, 'grad_norm': 0.000534688250805484, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:47<04:09, 3.67s/it] 87%|████████▋ | 453/520 [27:51<04:04, 3.65s/it] {'loss': 1.1916, 'grad_norm': 0.0005382875810573867, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [27:51<04:04, 3.65s/it] 87%|████████▋ | 454/520 [27:54<04:00, 3.64s/it] {'loss': 1.1066, 'grad_norm': 0.0005881607735974063, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [27:54<04:00, 3.64s/it] 88%|████████▊ | 455/520 [27:58<03:55, 3.63s/it] {'loss': 1.2437, 'grad_norm': 0.0005848861797547436, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [27:58<03:55, 3.63s/it] 88%|████████▊ | 456/520 [28:02<03:51, 3.62s/it] {'loss': 1.1801, 'grad_norm': 0.0005944522476059152, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:02<03:51, 3.62s/it] 88%|████████▊ | 457/520 [28:05<03:48, 3.63s/it] {'loss': 1.0741, 'grad_norm': 0.0004941482802584947, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:05<03:48, 3.63s/it] 88%|████████▊ | 458/520 [28:09<03:44, 3.63s/it] {'loss': 1.2937, 'grad_norm': 0.0006274441141522687, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:09<03:44, 3.63s/it] 88%|████████▊ | 459/520 [28:13<03:40, 3.62s/it] {'loss': 1.2264, 'grad_norm': 0.0005764625384527868, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:13<03:40, 3.62s/it] 88%|████████▊ | 460/520 [28:16<03:37, 3.63s/it] {'loss': 1.1188, 'grad_norm': 0.0005779309819291388, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:16<03:37, 3.63s/it] 89%|████████▊ | 461/520 [28:20<03:34, 3.63s/it] {'loss': 1.1585, 'grad_norm': 0.00041580177647810543, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:20<03:34, 3.63s/it] 89%|████████▉ | 462/520 [28:23<03:30, 3.63s/it] {'loss': 1.2559, 'grad_norm': 0.0005469818996272134, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:23<03:30, 3.63s/it] 89%|████████▉ | 463/520 [28:27<03:26, 3.63s/it] {'loss': 1.0921, 'grad_norm': 0.000606145064059784, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:27<03:26, 3.63s/it] 89%|████████▉ | 464/520 [28:31<03:23, 3.63s/it] {'loss': 1.2093, 'grad_norm': 0.0005905446237913184, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:31<03:23, 3.63s/it] 89%|████████▉ | 465/520 [28:34<03:20, 3.64s/it] {'loss': 1.3133, 'grad_norm': 0.0006024566552583673, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:34<03:20, 3.64s/it] 90%|████████▉ | 466/520 [28:38<03:16, 3.63s/it] {'loss': 1.211, 'grad_norm': 0.0005271250062200418, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:38<03:16, 3.63s/it] 90%|████████▉ | 467/520 [28:42<03:12, 3.64s/it] {'loss': 1.1471, 'grad_norm': 0.0005359196037568601, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:42<03:12, 3.64s/it] 90%|█████████ | 468/520 [28:45<03:08, 3.63s/it] {'loss': 1.1703, 'grad_norm': 0.0006580393266766185, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [28:45<03:08, 3.63s/it] 90%|█████████ | 469/520 [28:49<03:06, 3.66s/it] {'loss': 1.2447, 'grad_norm': 0.0006245302979583934, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [28:49<03:06, 3.66s/it] 90%|█████████ | 470/520 [28:53<03:02, 3.65s/it] {'loss': 1.115, 'grad_norm': 0.0005418259499842647, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [28:53<03:02, 3.65s/it] 91%|█████████ | 471/520 [28:56<02:58, 3.64s/it] {'loss': 1.1465, 'grad_norm': 0.0006328076525641056, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [28:56<02:58, 3.64s/it] 91%|█████████ | 472/520 [29:00<02:54, 3.64s/it] {'loss': 1.114, 'grad_norm': 0.0005614772801335832, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:00<02:54, 3.64s/it] 91%|█████████ | 473/520 [29:03<02:51, 3.64s/it] {'loss': 1.1861, 'grad_norm': 0.0006075344741941309, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:03<02:51, 3.64s/it] 91%|█████████ | 474/520 [29:07<02:47, 3.64s/it] {'loss': 1.18, 'grad_norm': 0.0005310754111107971, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:07<02:47, 3.64s/it] 91%|█████████▏| 475/520 [29:11<02:43, 3.63s/it] {'loss': 1.0961, 'grad_norm': 0.0005383840268182964, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:11<02:43, 3.63s/it] 92%|█████████▏| 476/520 [29:14<02:39, 3.63s/it] {'loss': 1.1684, 'grad_norm': 0.000609540590246044, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:14<02:39, 3.63s/it] 92%|█████████▏| 477/520 [29:18<02:36, 3.63s/it] {'loss': 1.1638, 'grad_norm': 0.0006388698343414017, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:18<02:36, 3.63s/it] 92%|█████████▏| 478/520 [29:22<02:32, 3.64s/it] {'loss': 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{'loss': 1.2808, 'grad_norm': 0.0005147255979271021, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:09<00:49, 3.82s/it] 98%|█████████▊| 508/520 [31:12<00:45, 3.82s/it] {'loss': 1.2692, 'grad_norm': 0.000604208556667267, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:12<00:45, 3.82s/it] 98%|█████████▊| 509/520 [31:16<00:42, 3.82s/it] {'loss': 1.2389, 'grad_norm': 0.0005601255572483951, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:16<00:42, 3.82s/it] 98%|█████████▊| 510/520 [31:20<00:38, 3.84s/it] {'loss': 1.1901, 'grad_norm': 0.0005875010191182996, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:20<00:38, 3.84s/it] 98%|█████████▊| 511/520 [31:24<00:34, 3.83s/it] {'loss': 1.1479, 'grad_norm': 0.0005731264984701641, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:24<00:34, 3.83s/it] 98%|█████████▊| 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[31:43<00:15, 3.80s/it] 99%|█████████▉| 517/520 [31:47<00:11, 3.82s/it] {'loss': 1.1776, 'grad_norm': 0.0005398089320406892, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [31:47<00:11, 3.82s/it] 100%|█████████▉| 518/520 [31:51<00:07, 3.79s/it] {'loss': 1.1753, 'grad_norm': 0.0006080693589612161, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [31:51<00:07, 3.79s/it] 100%|█████████▉| 519/520 [31:54<00:03, 3.77s/it] {'loss': 1.1548, 'grad_norm': 0.0005639478281678315, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [31:54<00:03, 3.77s/it] 100%|██████████| 520/520 [31:59<00:00, 4.02s/it] {'loss': 1.1412, 'grad_norm': 0.0004977502595333656, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [31:59<00:00, 4.02s/it] {'train_runtime': 1919.4, 'train_samples_per_second': 34.661, 'train_steps_per_second': 0.271, 'train_loss': 1.2320331494395549, 'epoch': 1.0} + 100%|██████████| 520/520 [31:59<00:00, 4.02s/it] 100%|██████████| 520/520 [31:59<00:00, 3.69s/it] +[2025-10-17 20:16:22,346] [INFO] [launch.py:348:main] Process 613611 exits successfully. +[2025-10-17 20:16:22,346] [INFO] [launch.py:348:main] Process 613606 exits successfully. +[2025-10-17 20:16:22,347] [INFO] [launch.py:348:main] Process 613607 exits successfully. +[2025-10-17 20:16:22,347] [INFO] [launch.py:348:main] Process 613610 exits successfully. +[2025-10-17 20:16:22,348] [INFO] [launch.py:348:main] Process 613612 exits successfully. +[2025-10-17 20:16:23,349] [INFO] [launch.py:348:main] Process 613608 exits successfully. +[2025-10-17 20:16:23,350] [INFO] [launch.py:348:main] Process 613609 exits successfully. +[2025-10-17 20:16:26,353] [INFO] [launch.py:348:main] Process 613605 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.3_2e-1_connector-7.0_1.3_2e-1_ablation_20251017_194249.log +Timestamp: 2025-10-17 20:16:28 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation_20251017_201628.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation_20251017_201628.log new file mode 100644 index 0000000000000000000000000000000000000000..399d39b3d5511e5a971756a6810646ef7615fffe --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation_20251017_201628.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation_20251017_201628.log +Timestamp: 2025-10-17 20:16:28 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:16:31,438] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:34,153] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 20:16:34,155] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 1.5 --temperature_mlp_text 1.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 1.5 --temperature_mlp_vision 1.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 1.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:16:36,740] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:37,791] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 20:16:37,791] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 20:16:37,791] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 20:16:37,791] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 20:16:37,791] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 20:16:37,791] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 20:16:37,791] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 20:16:37,793] [INFO] [launch.py:253:main] process 635394 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,795] [INFO] [launch.py:253:main] process 635395 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,797] [INFO] [launch.py:253:main] process 635396 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,799] [INFO] [launch.py:253:main] process 635397 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,801] [INFO] [launch.py:253:main] process 635398 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,803] [INFO] [launch.py:253:main] process 635399 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,805] [INFO] [launch.py:253:main] process 635400 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:16:37,807] [INFO] [launch.py:253:main] process 635401 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:16:44,519] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,788] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,887] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,890] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,931] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:44,949] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,964] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:44,974] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:45,003] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:16:45,186] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,293] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,301] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,301] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 20:16:45,361] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,372] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,385] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:16:45,410] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.5, 'temperature_mlp': 1.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.5, + "temperature_mlp": 1.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:635394:635394 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:635395:635395 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:635397:635397 [3] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:635398:635398 [4] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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[7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO Connected all trees 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Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635401:637010 [7] NCCL INFO ncclCommInitRank comm 0x55e59b61fc40 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635398:636990 [4] NCCL INFO ncclCommInitRank comm 0x55b2df052150 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635396:637008 [2] NCCL INFO ncclCommInitRank comm 0x55dc3931b680 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635397:636989 [3] NCCL INFO ncclCommInitRank comm 0x55561059d970 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:635395:636988 [1] NCCL INFO ncclCommInitRank comm 0x558d547d5d60 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635394:636987 [0] NCCL INFO ncclCommInitRank comm 0x55e8848413c0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:635400:637004 [6] NCCL INFO ncclCommInitRank comm 0x56049984a560 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +ywang29-vrdb-test1-worker-0:635399:637009 [5] NCCL INFO ncclCommInitRank comm 0x55fc722fabc0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xe8bfffadbf35d2cc - Init COMPLETE +[2025-10-17 20:17:30,656] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 20:17:32,420] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 20:17:50,162 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 20:17:50,169 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters 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+language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635394:641934 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635401:641938 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635396:641939 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635400:641937 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635395:641941 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635397:641940 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635398:641936 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:635399:641935 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:31<43:29, 5.07s/it] 1%| | 6/520 [00:35<40:27, 4.72s/it] {'loss': 1.4277, 'grad_norm': 0.0007081528980803784, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:35<40:27, 4.72s/it] 1%|▏ | 7/520 [00:40<38:31, 4.51s/it] {'loss': 1.5205, 'grad_norm': 0.0008534506233064188, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:40<38:31, 4.51s/it] 2%|▏ | 8/520 [00:44<38:57, 4.57s/it] {'loss': 1.5289, 'grad_norm': 0.0005911160041114217, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:44<38:57, 4.57s/it] 2%|▏ | 9/520 [00:49<38:53, 4.57s/it] {'loss': 1.5881, 'grad_norm': 0.000507468946615292, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:49<38:53, 4.57s/it] 2%|▏ | 10/520 [00:53<37:23, 4.40s/it] {'loss': 1.4312, 'grad_norm': 0.0004848635288438626, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:53<37:23, 4.40s/it] 2%|▏ | 11/520 [00:57<36:17, 4.28s/it] {'loss': 1.4661, 'grad_norm': 0.000403935002260238, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:57<36:17, 4.28s/it] 2%|▏ | 12/520 [01:01<35:07, 4.15s/it] {'loss': 1.3419, 'grad_norm': 0.0003549218620158191, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:01<35:07, 4.15s/it][2025-10-17 20:19:01,124] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:05<36:03, 4.27s/it] {'loss': 1.4221, 'grad_norm': 0.000333235551747163, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:05<36:03, 4.27s/it] 3%|▎ | 14/520 [01:09<35:00, 4.15s/it] {'loss': 1.4572, 'grad_norm': 0.0003858404931572449, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:09<35:00, 4.15s/it] 3%|▎ | 15/520 [01:13<34:13, 4.07s/it] {'loss': 1.3732, 'grad_norm': 0.00028290147219999407, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:13<34:13, 4.07s/it] 3%|▎ | 16/520 [01:17<33:32, 3.99s/it] {'loss': 1.3461, 'grad_norm': 0.0003518710722649121, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:17<33:32, 3.99s/it] 3%|▎ | 17/520 [01:21<33:46, 4.03s/it] {'loss': 1.4808, 'grad_norm': 0.0003894940456468106, 'learning_rate': 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4%|▍ | 23/520 [01:44<30:57, 3.74s/it] {'loss': 1.3922, 'grad_norm': 0.0003511274740768234, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:44<30:57, 3.74s/it] 5%|▍ | 24/520 [01:47<30:40, 3.71s/it] {'loss': 1.3118, 'grad_norm': 0.0004000704565405112, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:47<30:40, 3.71s/it] 5%|▍ | 25/520 [01:51<30:31, 3.70s/it] {'loss': 1.3957, 'grad_norm': 0.00044212578703507787, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:51<30:31, 3.70s/it] 5%|▌ | 26/520 [01:55<30:31, 3.71s/it] {'loss': 1.3326, 'grad_norm': 0.0003306489539391517, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:55<30:31, 3.71s/it] 5%|▌ | 27/520 [01:58<30:45, 3.74s/it] {'loss': 1.2653, 'grad_norm': 0.0003272288506089434, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:58<30:45, 3.74s/it] 5%|▌ | 28/520 [02:02<30:28, 3.72s/it] {'loss': 1.2964, 'grad_norm': 0.00035298761177860117, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [02:02<30:28, 3.72s/it] 6%|▌ | 29/520 [02:06<30:19, 3.71s/it] {'loss': 1.315, 'grad_norm': 0.00033901239095803436, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [02:06<30:19, 3.71s/it] 6%|▌ | 30/520 [02:09<30:09, 3.69s/it] {'loss': 1.3769, 'grad_norm': 0.00031627881617904673, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:09<30:09, 3.69s/it] 6%|▌ | 31/520 [02:13<30:00, 3.68s/it] {'loss': 1.278, 'grad_norm': 0.00033506951129138663, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:13<30:00, 3.68s/it] 6%|▌ | 32/520 [02:17<30:02, 3.69s/it] {'loss': 1.1986, 'grad_norm': 0.0003315459440078522, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:17<30:02, 3.69s/it] 6%|▋ | 33/520 [02:21<30:02, 3.70s/it] {'loss': 1.2728, 'grad_norm': 0.00035089707163458354, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 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{'loss': 1.1006, 'grad_norm': 0.0005018066707602548, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:38<12:38, 3.67s/it] 60%|██████ | 314/520 [19:42<12:57, 3.77s/it] {'loss': 1.1444, 'grad_norm': 0.0005314821891114626, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:42<12:57, 3.77s/it] 61%|██████ | 315/520 [19:46<12:43, 3.72s/it] {'loss': 1.1787, 'grad_norm': 0.0005955850653421691, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:46<12:43, 3.72s/it] 61%|██████ | 316/520 [19:50<13:01, 3.83s/it] {'loss': 1.1303, 'grad_norm': 0.0005795700748403927, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:50<13:01, 3.83s/it] 61%|██████ | 317/520 [19:53<12:45, 3.77s/it] {'loss': 1.1318, 'grad_norm': 0.0004949671501093627, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:53<12:45, 3.77s/it] 61%|██████ | 318/520 [19:57<12:31, 3.72s/it] {'loss': 1.2396, 'grad_norm': 0.0005764754367174544, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:57<12:31, 3.72s/it] 61%|██████▏ | 319/520 [20:01<12:49, 3.83s/it] {'loss': 1.1254, 'grad_norm': 0.0004988369601696528, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:01<12:49, 3.83s/it] 62%|██████▏ | 320/520 [20:05<12:33, 3.77s/it] {'loss': 1.0668, 'grad_norm': 0.0005438387014844803, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:05<12:33, 3.77s/it] 62%|██████▏ | 321/520 [20:08<12:22, 3.73s/it] {'loss': 1.2625, 'grad_norm': 0.0005424662726325663, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:08<12:22, 3.73s/it] 62%|██████▏ | 322/520 [20:12<12:13, 3.70s/it] {'loss': 1.0809, 'grad_norm': 0.0005158424910479693, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:12<12:13, 3.70s/it] 62%|██████▏ | 323/520 [20:15<12:03, 3.67s/it] {'loss': 1.1522, 'grad_norm': 0.0005394008877990655, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:15<12:03, 3.67s/it] 62%|██████▏ | 324/520 [20:19<11:58, 3.66s/it] {'loss': 1.2089, 'grad_norm': 0.0005605150135767116, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:19<11:58, 3.66s/it] 62%|██████▎ | 325/520 [20:23<11:51, 3.65s/it] {'loss': 1.2028, 'grad_norm': 0.0005745225668856385, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:23<11:51, 3.65s/it] 63%|██████▎ | 326/520 [20:26<11:46, 3.64s/it] {'loss': 1.2062, 'grad_norm': 0.000592083582794191, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:26<11:46, 3.64s/it] 63%|██████▎ | 327/520 [20:30<11:42, 3.64s/it] {'loss': 1.1816, 'grad_norm': 0.0005640534573746684, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:30<11:42, 3.64s/it] 63%|██████▎ | 328/520 [20:34<11:37, 3.63s/it] {'loss': 1.2407, 'grad_norm': 0.000569221983400971, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:34<11:37, 3.63s/it] 63%|██████▎ | 329/520 [20:37<11:34, 3.64s/it] {'loss': 1.1272, 'grad_norm': 0.00048801399846938663, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:37<11:34, 3.64s/it] 63%|██████▎ | 330/520 [20:41<11:32, 3.64s/it] {'loss': 1.2024, 'grad_norm': 0.0005182442357481497, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:41<11:32, 3.64s/it] 64%|██████▎ | 331/520 [20:45<11:26, 3.63s/it] {'loss': 1.1609, 'grad_norm': 0.0005810874516434794, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:45<11:26, 3.63s/it] 64%|██████▍ | 332/520 [20:48<11:25, 3.64s/it] {'loss': 1.2107, 'grad_norm': 0.0004978612724711981, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:48<11:25, 3.64s/it] 64%|██████▍ | 333/520 [20:52<11:21, 3.65s/it] {'loss': 1.2954, 'grad_norm': 0.0005803209070193992, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:52<11:21, 3.65s/it] 64%|██████▍ | 334/520 [20:55<11:18, 3.65s/it] {'loss': 1.206, 'grad_norm': 0.0005858257883361585, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:55<11:18, 3.65s/it] 64%|██████▍ | 335/520 [20:59<11:16, 3.66s/it] {'loss': 1.208, 'grad_norm': 0.0005349488245030851, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:59<11:16, 3.66s/it] 65%|██████▍ | 336/520 [21:03<11:13, 3.66s/it] {'loss': 1.1161, 'grad_norm': 0.0006025913455766587, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:03<11:13, 3.66s/it] 65%|██████▍ | 337/520 [21:06<11:08, 3.65s/it] {'loss': 1.1098, 'grad_norm': 0.0005615321603660399, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:06<11:08, 3.65s/it] 65%|██████▌ | 338/520 [21:10<11:05, 3.66s/it] {'loss': 1.2132, 'grad_norm': 0.0005510831588353835, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:10<11:05, 3.66s/it] 65%|██████▌ | 339/520 [21:14<11:00, 3.65s/it] {'loss': 1.1555, 'grad_norm': 0.000563643086385732, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:14<11:00, 3.65s/it] 65%|██████▌ | 340/520 [21:17<10:58, 3.66s/it] {'loss': 1.1439, 'grad_norm': 0.0005336658652302502, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:17<10:58, 3.66s/it] 66%|██████▌ | 341/520 [21:21<10:56, 3.66s/it] {'loss': 1.1777, 'grad_norm': 0.0005869627634750097, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:21<10:56, 3.66s/it] 66%|██████▌ | 342/520 [21:25<10:49, 3.65s/it] {'loss': 1.189, 'grad_norm': 0.0006300574819230477, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:25<10:49, 3.65s/it] 66%|██████▌ | 343/520 [21:28<10:47, 3.66s/it] {'loss': 1.1372, 'grad_norm': 0.000448082377619964, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:28<10:47, 3.66s/it] 66%|██████▌ | 344/520 [21:32<10:42, 3.65s/it] {'loss': 1.1343, 'grad_norm': 0.00050510548188848, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:32<10:42, 3.65s/it] 66%|██████▋ | 345/520 [21:36<10:37, 3.65s/it] {'loss': 1.2307, 'grad_norm': 0.0005643846915990024, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:36<10:37, 3.65s/it] 67%|██████▋ | 346/520 [21:39<10:33, 3.64s/it] {'loss': 1.1569, 'grad_norm': 0.000525684526240461, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:39<10:33, 3.64s/it] 67%|██████▋ | 347/520 [21:43<10:29, 3.64s/it] {'loss': 1.1508, 'grad_norm': 0.0005069328184040273, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:43<10:29, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:47<10:25, 3.64s/it] {'loss': 1.1072, 'grad_norm': 0.0006510514747530836, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:47<10:25, 3.64s/it] 67%|██████▋ | 349/520 [21:50<10:22, 3.64s/it] {'loss': 1.1417, 'grad_norm': 0.0005551582754693063, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:50<10:22, 3.64s/it] 67%|██████▋ | 350/520 [21:54<10:17, 3.63s/it] {'loss': 1.1865, 'grad_norm': 0.0005762809530655358, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:54<10:17, 3.63s/it] 68%|██████▊ | 351/520 [21:58<10:15, 3.64s/it] {'loss': 1.0989, 'grad_norm': 0.0005197402358180803, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:58<10:15, 3.64s/it] 68%|██████▊ | 352/520 [22:01<10:12, 3.64s/it] {'loss': 1.2102, 'grad_norm': 0.0005157164629856259, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:01<10:12, 3.64s/it] 68%|██████▊ | 353/520 [22:05<10:10, 3.66s/it] {'loss': 1.1335, 'grad_norm': 0.0004546119124796901, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:05<10:10, 3.66s/it] 68%|██████▊ | 354/520 [22:08<10:06, 3.65s/it] {'loss': 1.2215, 'grad_norm': 0.0004957074177715497, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:08<10:06, 3.65s/it] 68%|██████▊ | 355/520 [22:12<10:01, 3.64s/it] {'loss': 1.1619, 'grad_norm': 0.0005496818159235932, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:12<10:01, 3.64s/it] 68%|██████▊ | 356/520 [22:16<09:57, 3.64s/it] {'loss': 1.1615, 'grad_norm': 0.0005626858753438323, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:16<09:57, 3.64s/it] 69%|██████▊ | 357/520 [22:19<09:51, 3.63s/it] {'loss': 1.1987, 'grad_norm': 0.0005260226983813609, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:19<09:51, 3.63s/it] 69%|██████▉ | 358/520 [22:23<09:48, 3.64s/it] {'loss': 1.1243, 'grad_norm': 0.0005573259705913887, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:23<09:48, 3.64s/it] 69%|██████▉ | 359/520 [22:27<09:45, 3.63s/it] {'loss': 1.1656, 'grad_norm': 0.0005402813942974176, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:27<09:45, 3.63s/it] 69%|██████▉ | 360/520 [22:30<09:41, 3.64s/it] {'loss': 1.1727, 'grad_norm': 0.000536894369537975, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:30<09:41, 3.64s/it] 69%|██████▉ | 361/520 [22:34<09:38, 3.64s/it] {'loss': 1.1909, 'grad_norm': 0.0004807986425773743, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:34<09:38, 3.64s/it] 70%|██████▉ | 362/520 [22:38<09:39, 3.67s/it] {'loss': 1.1704, 'grad_norm': 0.0005979167108580818, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:38<09:39, 3.67s/it] 70%|██████▉ | 363/520 [22:41<09:33, 3.65s/it] {'loss': 1.2001, 'grad_norm': 0.0005434236323462737, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:41<09:33, 3.65s/it] 70%|███████ | 364/520 [22:45<09:31, 3.66s/it] {'loss': 1.2031, 'grad_norm': 0.0005425867712711825, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:45<09:31, 3.66s/it] 70%|███████ | 365/520 [22:49<09:26, 3.66s/it] {'loss': 1.2524, 'grad_norm': 0.000562865084898431, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:49<09:26, 3.66s/it] 70%|███████ | 366/520 [22:52<09:23, 3.66s/it] {'loss': 1.22, 'grad_norm': 0.0005290558911675367, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:52<09:23, 3.66s/it] 71%|███████ | 367/520 [22:56<09:18, 3.65s/it] {'loss': 1.2191, 'grad_norm': 0.000570566441680011, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:56<09:18, 3.65s/it] 71%|███████ | 368/520 [23:00<09:15, 3.65s/it] {'loss': 1.0723, 'grad_norm': 0.0005572704595295433, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:00<09:15, 3.65s/it] 71%|███████ | 369/520 [23:03<09:12, 3.66s/it] {'loss': 1.1669, 'grad_norm': 0.0004893509817050712, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:03<09:12, 3.66s/it] 71%|███████ | 370/520 [23:07<09:07, 3.65s/it] {'loss': 1.133, 'grad_norm': 0.0005211696532915506, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:07<09:07, 3.65s/it] 71%|███████▏ | 371/520 [23:10<09:03, 3.65s/it] {'loss': 1.1212, 'grad_norm': 0.000577550151235099, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:11<09:03, 3.65s/it] 72%|███████▏ | 372/520 [23:14<09:00, 3.65s/it] {'loss': 1.2327, 'grad_norm': 0.0004954767431741389, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:14<09:00, 3.65s/it] 72%|███████▏ | 373/520 [23:18<08:56, 3.65s/it] {'loss': 1.1224, 'grad_norm': 0.0005759431901072766, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:18<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:21<08:52, 3.65s/it] {'loss': 1.2173, 'grad_norm': 0.0005698569574050251, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:21<08:52, 3.65s/it] 72%|███████▏ | 375/520 [23:25<08:48, 3.64s/it] {'loss': 1.1331, 'grad_norm': 0.0005642249191987784, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:25<08:48, 3.64s/it] 72%|███████▏ | 376/520 [23:29<08:44, 3.64s/it] {'loss': 1.2388, 'grad_norm': 0.0005275657864418583, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:29<08:44, 3.64s/it] 72%|███████▎ | 377/520 [23:32<08:40, 3.64s/it] {'loss': 1.1685, 'grad_norm': 0.0005807424922163564, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:32<08:40, 3.64s/it] 73%|███████▎ | 378/520 [23:36<08:37, 3.64s/it] {'loss': 1.2335, 'grad_norm': 0.0005185028761991974, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:36<08:37, 3.64s/it] 73%|███████▎ | 379/520 [23:40<08:33, 3.64s/it] {'loss': 1.1983, 'grad_norm': 0.0005116123625318147, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:40<08:33, 3.64s/it] 73%|███████▎ | 380/520 [23:43<08:31, 3.66s/it] {'loss': 1.2125, 'grad_norm': 0.0005465597458894187, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:43<08:31, 3.66s/it] 73%|███████▎ | 381/520 [23:47<08:36, 3.72s/it] {'loss': 1.2095, 'grad_norm': 0.0005185301082918315, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:47<08:36, 3.72s/it] 73%|███████▎ | 382/520 [23:51<08:39, 3.77s/it] {'loss': 1.1842, 'grad_norm': 0.0005017048737289576, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:51<08:39, 3.77s/it] 74%|███████▎ | 383/520 [23:55<08:39, 3.79s/it] {'loss': 1.0548, 'grad_norm': 0.0006150186112301162, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:55<08:39, 3.79s/it] 74%|███████▍ | 384/520 [23:59<08:39, 3.82s/it] {'loss': 1.2023, 'grad_norm': 0.00046210601085913553, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:59<08:39, 3.82s/it] 74%|███████▍ | 385/520 [24:03<08:37, 3.83s/it] {'loss': 1.1958, 'grad_norm': 0.0005080342726515744, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:03<08:37, 3.83s/it] 74%|███████▍ | 386/520 [24:07<08:34, 3.84s/it] {'loss': 1.1464, 'grad_norm': 0.00047039200103532095, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:07<08:34, 3.84s/it] 74%|███████▍ | 387/520 [24:10<08:33, 3.86s/it] {'loss': 1.2319, 'grad_norm': 0.0005179460353361595, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:10<08:33, 3.86s/it] 75%|███████▍ | 388/520 [24:14<08:29, 3.86s/it] {'loss': 1.107, 'grad_norm': 0.0005285910898652782, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:14<08:29, 3.86s/it] 75%|███████▍ | 389/520 [24:18<08:26, 3.86s/it] {'loss': 1.1537, 'grad_norm': 0.0006639644665387644, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:18<08:26, 3.86s/it] 75%|███████▌ | 390/520 [24:22<08:22, 3.86s/it] {'loss': 1.2225, 'grad_norm': 0.0005262503044822343, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:22<08:22, 3.86s/it] 75%|███████▌ | 391/520 [24:26<08:20, 3.88s/it] {'loss': 1.28, 'grad_norm': 0.0005546631535125128, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:26<08:20, 3.88s/it] 75%|███████▌ | 392/520 [24:30<08:16, 3.88s/it] {'loss': 1.1077, 'grad_norm': 0.0005366880763809982, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:30<08:16, 3.88s/it] 76%|███████▌ | 393/520 [24:34<08:11, 3.87s/it] {'loss': 1.0947, 'grad_norm': 0.00046000501504024686, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:34<08:11, 3.87s/it] 76%|███████▌ | 394/520 [24:38<08:07, 3.87s/it] {'loss': 1.1787, 'grad_norm': 0.0005735810857574496, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:38<08:07, 3.87s/it] 76%|███████▌ | 395/520 [24:41<08:03, 3.87s/it] {'loss': 1.1418, 'grad_norm': 0.0005799607911635895, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:41<08:03, 3.87s/it] 76%|███████▌ | 396/520 [24:45<08:00, 3.87s/it] {'loss': 1.22, 'grad_norm': 0.0005784530723220154, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:45<08:00, 3.87s/it] 76%|███████▋ | 397/520 [24:49<07:56, 3.88s/it] {'loss': 1.1945, 'grad_norm': 0.0005317462185778751, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:49<07:56, 3.88s/it] 77%|███████▋ | 398/520 [24:53<07:44, 3.81s/it] {'loss': 1.1898, 'grad_norm': 0.0005763819596199918, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:53<07:44, 3.81s/it] 77%|███████▋ | 399/520 [24:56<07:35, 3.76s/it] {'loss': 1.1252, 'grad_norm': 0.0005134329113853189, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:56<07:35, 3.76s/it] 77%|███████▋ | 400/520 [25:00<07:27, 3.73s/it] {'loss': 1.1583, 'grad_norm': 0.0004931263775175427, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:00<07:27, 3.73s/it] 77%|███████▋ | 401/520 [25:04<07:20, 3.70s/it] {'loss': 1.0332, 'grad_norm': 0.0006033387827889023, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:04<07:20, 3.70s/it] 77%|███████▋ | 402/520 [25:07<07:14, 3.69s/it] {'loss': 1.1599, 'grad_norm': 0.0005642840630540127, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:07<07:14, 3.69s/it] 78%|███████▊ | 403/520 [25:11<07:09, 3.67s/it] {'loss': 1.1809, 'grad_norm': 0.0005953622557526115, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:11<07:09, 3.67s/it] 78%|███████▊ | 404/520 [25:15<07:04, 3.66s/it] {'loss': 1.0954, 'grad_norm': 0.0006350515851836502, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:15<07:04, 3.66s/it] 78%|███████▊ | 405/520 [25:18<07:00, 3.66s/it] {'loss': 1.1401, 'grad_norm': 0.0005220325403718001, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:18<07:00, 3.66s/it] 78%|███████▊ | 406/520 [25:22<06:56, 3.65s/it] {'loss': 1.065, 'grad_norm': 0.0006357227359541435, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:22<06:56, 3.65s/it] 78%|███████▊ | 407/520 [25:26<06:51, 3.64s/it] {'loss': 1.2599, 'grad_norm': 0.0005599820853189976, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:26<06:51, 3.64s/it] 78%|███████▊ | 408/520 [25:29<06:47, 3.64s/it] {'loss': 1.1748, 'grad_norm': 0.0006112387405980543, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:29<06:47, 3.64s/it] 79%|███████▊ | 409/520 [25:33<06:43, 3.64s/it] {'loss': 1.2888, 'grad_norm': 0.0005828147156843925, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:33<06:43, 3.64s/it] 79%|███████▉ | 410/520 [25:36<06:40, 3.64s/it] {'loss': 1.0342, 'grad_norm': 0.0005513961326491883, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:36<06:40, 3.64s/it] 79%|███████▉ | 411/520 [25:40<06:35, 3.63s/it] {'loss': 1.2714, 'grad_norm': 0.0005870553558092646, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:40<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:44<06:32, 3.64s/it] {'loss': 1.1795, 'grad_norm': 0.0005409755683853407, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:44<06:32, 3.64s/it] 79%|███████▉ | 413/520 [25:47<06:30, 3.65s/it] {'loss': 1.1564, 'grad_norm': 0.0005107308736130552, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:47<06:30, 3.65s/it] 80%|███████▉ | 414/520 [25:51<06:26, 3.65s/it] {'loss': 0.9682, 'grad_norm': 0.00044630227118051146, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:51<06:26, 3.65s/it] 80%|███████▉ | 415/520 [25:55<06:22, 3.65s/it] {'loss': 1.1632, 'grad_norm': 0.000523123193813023, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:55<06:22, 3.65s/it] 80%|████████ | 416/520 [25:58<06:19, 3.65s/it] {'loss': 1.0672, 'grad_norm': 0.0006046448124639019, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:58<06:19, 3.65s/it] 80%|████████ | 417/520 [26:02<06:14, 3.64s/it] {'loss': 1.2292, 'grad_norm': 0.0005425072337482146, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:02<06:14, 3.64s/it] 80%|████████ | 418/520 [26:06<06:10, 3.63s/it] {'loss': 1.2248, 'grad_norm': 0.000514748198693584, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:06<06:10, 3.63s/it] 81%|████████ | 419/520 [26:09<06:07, 3.64s/it] {'loss': 1.2161, 'grad_norm': 0.0005981908887285972, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:09<06:07, 3.64s/it] 81%|████████ | 420/520 [26:13<06:03, 3.64s/it] {'loss': 1.1076, 'grad_norm': 0.0005654132002760783, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:13<06:03, 3.64s/it] 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] {'loss': 1.0458, 'grad_norm': 0.0005804979163055278, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] 81%|████████ | 422/520 [26:20<05:56, 3.63s/it] {'loss': 1.1692, 'grad_norm': 0.0005825794088657525, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:20<05:56, 3.63s/it] 81%|████████▏ | 423/520 [26:24<05:53, 3.64s/it] {'loss': 1.1359, 'grad_norm': 0.0005882555137576232, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:24<05:53, 3.64s/it] 82%|████████▏ | 424/520 [26:27<05:50, 3.65s/it] {'loss': 1.2395, 'grad_norm': 0.0005040121315011834, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:27<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:31<05:46, 3.65s/it] {'loss': 1.153, 'grad_norm': 0.0005484684720180029, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:31<05:46, 3.65s/it] 82%|████████▏ | 426/520 [26:35<05:42, 3.64s/it] {'loss': 1.1879, 'grad_norm': 0.000716606473400457, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:35<05:42, 3.64s/it] 82%|████████▏ | 427/520 [26:38<05:38, 3.64s/it] {'loss': 1.0853, 'grad_norm': 0.0005276204833668915, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:38<05:38, 3.64s/it] 82%|████████▏ | 428/520 [26:42<05:34, 3.64s/it] {'loss': 1.0807, 'grad_norm': 0.0005947998987833871, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:42<05:34, 3.64s/it] 82%|████████▎ | 429/520 [26:46<05:31, 3.64s/it] {'loss': 1.1756, 'grad_norm': 0.0005568423515171353, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:46<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:49<05:27, 3.63s/it] {'loss': 1.1781, 'grad_norm': 0.0005250701760167156, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:49<05:27, 3.63s/it] 83%|████████▎ | 431/520 [26:53<05:24, 3.65s/it] {'loss': 1.1293, 'grad_norm': 0.0005289476485179897, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:53<05:24, 3.65s/it] 83%|████████▎ | 432/520 [26:57<05:21, 3.66s/it] {'loss': 1.0828, 'grad_norm': 0.0005688003171705086, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:57<05:21, 3.66s/it] 83%|████████▎ | 433/520 [27:00<05:18, 3.66s/it] {'loss': 1.2165, 'grad_norm': 0.0005471454810695692, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:00<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:04<05:18, 3.70s/it] {'loss': 0.9681, 'grad_norm': 0.0005669215567214728, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:04<05:18, 3.70s/it] 84%|████████▎ | 435/520 [27:08<05:14, 3.70s/it] {'loss': 1.2468, 'grad_norm': 0.000595785790872307, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:08<05:14, 3.70s/it] 84%|████████▍ | 436/520 [27:11<05:10, 3.70s/it] {'loss': 1.0597, 'grad_norm': 0.0005779502671595182, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:11<05:10, 3.70s/it] 84%|████████▍ | 437/520 [27:15<05:05, 3.69s/it] {'loss': 1.2698, 'grad_norm': 0.0005588650016021444, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:15<05:05, 3.69s/it] 84%|████████▍ | 438/520 [27:19<05:07, 3.75s/it] {'loss': 1.0904, 'grad_norm': 0.0005516657385935329, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:19<05:07, 3.75s/it] 84%|████████▍ | 439/520 [27:23<05:03, 3.75s/it] {'loss': 1.114, 'grad_norm': 0.00044345871982832784, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:23<05:03, 3.75s/it] 85%|████████▍ | 440/520 [27:26<04:57, 3.72s/it] {'loss': 1.1262, 'grad_norm': 0.0005939607781118713, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:26<04:57, 3.72s/it] 85%|████████▍ | 441/520 [27:30<04:52, 3.70s/it] {'loss': 1.1243, 'grad_norm': 0.0005613545774849971, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:30<04:52, 3.70s/it] 85%|████████▌ | 442/520 [27:34<04:47, 3.69s/it] {'loss': 1.1899, 'grad_norm': 0.0006102360001663328, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:34<04:47, 3.69s/it] 85%|████████▌ | 443/520 [27:37<04:42, 3.67s/it] {'loss': 1.1969, 'grad_norm': 0.0005322325799683426, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:37<04:42, 3.67s/it] 85%|████████▌ | 444/520 [27:41<04:38, 3.66s/it] 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{'loss': 1.2726, 'grad_norm': 0.00048457491885945634, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:38<00:47, 3.67s/it] 98%|█████████▊| 508/520 [31:42<00:43, 3.66s/it] {'loss': 1.2603, 'grad_norm': 0.0005640514421901854, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:42<00:43, 3.66s/it] 98%|█████████▊| 509/520 [31:46<00:40, 3.66s/it] {'loss': 1.2305, 'grad_norm': 0.0005314206819293372, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:46<00:40, 3.66s/it] 98%|█████████▊| 510/520 [31:49<00:36, 3.66s/it] {'loss': 1.1802, 'grad_norm': 0.0005484311600056876, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:49<00:36, 3.66s/it] 98%|█████████▊| 511/520 [31:53<00:33, 3.71s/it] {'loss': 1.1419, 'grad_norm': 0.0005358976263981136, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:53<00:33, 3.71s/it] 98%|█████████▊| 512/520 [31:57<00:29, 3.74s/it] {'loss': 1.0387, 'grad_norm': 0.0005360256110286185, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [31:57<00:29, 3.74s/it] 99%|█████████▊| 513/520 [32:01<00:26, 3.76s/it] {'loss': 1.2311, 'grad_norm': 0.0006016498131430344, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [32:01<00:26, 3.76s/it] 99%|█████████▉| 514/520 [32:05<00:22, 3.78s/it] {'loss': 1.1994, 'grad_norm': 0.0005079872453965339, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 99%|█████████▉| 514/520 [32:05<00:22, 3.78s/it] 99%|█████████▉| 515/520 [32:08<00:18, 3.79s/it] {'loss': 1.2518, 'grad_norm': 0.0006545250127725381, 'learning_rate': 4.856389714723575e-05, 'epoch': 0.99} + 99%|█████████▉| 515/520 [32:08<00:18, 3.79s/it] 99%|█████████▉| 516/520 [32:12<00:15, 3.80s/it] {'loss': 1.1681, 'grad_norm': 0.0005550393778864573, 'learning_rate': 3.108179991837545e-05, 'epoch': 0.99} + 99%|█████████▉| 516/520 [32:12<00:15, 3.80s/it] 99%|█████████▉| 517/520 [32:16<00:11, 3.79s/it] {'loss': 1.1705, 'grad_norm': 0.0005074621550275209, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:16<00:11, 3.79s/it] 100%|█████████▉| 518/520 [32:20<00:07, 3.78s/it] {'loss': 1.1713, 'grad_norm': 0.0005758607349527264, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:20<00:07, 3.78s/it] 100%|█████████▉| 519/520 [32:24<00:03, 3.78s/it] {'loss': 1.1479, 'grad_norm': 0.000531244074249187, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:24<00:03, 3.78s/it] 100%|██████████| 520/520 [32:28<00:00, 4.05s/it] {'loss': 1.1347, 'grad_norm': 0.00047068807250193374, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:28<00:00, 4.05s/it] {'train_runtime': 1948.8207, 'train_samples_per_second': 34.138, 'train_steps_per_second': 0.267, 'train_loss': 1.2193498324889402, 'epoch': 1.0} + 100%|██████████| 520/520 [32:28<00:00, 4.05s/it] 100%|██████████| 520/520 [32:28<00:00, 3.75s/it] +[2025-10-17 20:50:29,983] [INFO] [launch.py:348:main] Process 635395 exits successfully. +[2025-10-17 20:50:29,983] [INFO] [launch.py:348:main] Process 635397 exits successfully. +[2025-10-17 20:50:30,985] [INFO] [launch.py:348:main] Process 635400 exits successfully. +[2025-10-17 20:50:30,985] [INFO] [launch.py:348:main] Process 635398 exits successfully. +[2025-10-17 20:50:30,986] [INFO] [launch.py:348:main] Process 635396 exits successfully. +[2025-10-17 20:50:30,986] [INFO] [launch.py:348:main] Process 635399 exits successfully. +[2025-10-17 20:50:30,987] [INFO] [launch.py:348:main] Process 635401 exits successfully. +[2025-10-17 20:50:34,991] [INFO] [launch.py:348:main] Process 635394 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.5_2e-1_connector-7.0_1.5_2e-1_ablation_20251017_201628.log +Timestamp: 2025-10-17 20:50:37 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation_20251017_205037.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation_20251017_205037.log new file mode 100644 index 0000000000000000000000000000000000000000..3884fc964acb7b2385a80f870fee476b8d406e29 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation_20251017_205037.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation_20251017_205037.log +Timestamp: 2025-10-17 20:50:37 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:50:40,235] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:43,161] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 20:50:43,162] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 1.7 --temperature_mlp_text 1.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 1.7 --temperature_mlp_vision 1.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 1.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:50:45,712] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:46,779] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 20:50:46,779] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 20:50:46,779] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 20:50:46,779] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 20:50:46,779] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 20:50:46,779] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 20:50:46,779] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 20:50:46,781] [INFO] [launch.py:253:main] process 657655 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,783] [INFO] [launch.py:253:main] process 657656 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,785] [INFO] [launch.py:253:main] process 657657 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,787] [INFO] [launch.py:253:main] process 657658 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,789] [INFO] [launch.py:253:main] process 657659 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,791] [INFO] [launch.py:253:main] process 657660 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,793] [INFO] [launch.py:253:main] process 657661 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 20:50:46,795] [INFO] [launch.py:253:main] process 657662 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 20:50:53,526] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,645] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,826] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,835] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,835] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,885] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,885] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,921] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 20:50:53,946] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,068] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,240] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,242] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,243] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 20:50:54,243] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,291] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,292] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 20:50:54,329] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.7, 'temperature_mlp': 1.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.7, + "temperature_mlp": 1.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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+ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Channel 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23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 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512 +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657657:659248 [2] NCCL INFO ncclCommInitRank comm 0x5557b2f3cf40 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657658:659231 [3] NCCL INFO ncclCommInitRank comm 0x55e1a1cf4290 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657660:659234 [5] NCCL INFO ncclCommInitRank comm 0x555d14279040 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657661:659233 [6] NCCL INFO ncclCommInitRank comm 0x55c35133ae60 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657656:659249 [1] NCCL INFO ncclCommInitRank comm 0x562f083afb80 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657659:659232 [4] NCCL INFO ncclCommInitRank comm 0x55c0c3d869f0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657655:659226 [0] NCCL INFO ncclCommInitRank comm 0x56227530a0d0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x4b70457474ad6212 - Init COMPLETE +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:657662:659247 [7] NCCL INFO ncclCommInitRank comm 0x559c92cbb110 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x4b70457474ad6212 - Init COMPLETE +[2025-10-17 20:51:39,389] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 20:51:41,134] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 20:51:58,687 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 20:51:58,695 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters 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+language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters 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+language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters 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+language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters 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+language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:005->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657655:664161 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657662:664164 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657657:664163 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657661:664165 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657658:664166 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657659:664167 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657656:664162 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:657660:664168 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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2/520 [00:18<1:12:05, 8.35s/it] {'loss': 2.0514, 'grad_norm': 0.00596785752590268, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:12:05, 8.35s/it] 1%| | 3/520 [00:22<54:14, 6.29s/it] {'loss': 2.1885, 'grad_norm': 0.00685784329481581, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<54:14, 6.29s/it] 1%| | 4/520 [00:26<45:40, 5.31s/it] {'loss': 2.0657, 'grad_norm': 0.005689427319491335, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:26<45:40, 5.31s/it] 1%| | 5/520 [00:30<40:57, 4.77s/it] {'loss': 2.2294, 'grad_norm': 0.006253030055077659, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:30<40:57, 4.77s/it] 1%| | 6/520 [00:33<38:08, 4.45s/it] {'loss': 1.6762, 'grad_norm': 0.0032089306940803254, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<38:08, 4.45s/it] 1%|▏ | 7/520 [00:37<36:13, 4.24s/it] {'loss': 1.6428, 'grad_norm': 0.003478233886068732, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:37<36:13, 4.24s/it] 2%|▏ | 8/520 [00:42<36:41, 4.30s/it] {'loss': 1.5792, 'grad_norm': 0.0013486423126955584, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:42<36:41, 4.30s/it] 2%|▏ | 9/520 [00:46<36:41, 4.31s/it] {'loss': 1.6308, 'grad_norm': 0.001109853953296793, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:46<36:41, 4.31s/it] 2%|▏ | 10/520 [00:50<35:18, 4.15s/it] {'loss': 1.4855, 'grad_norm': 0.0011157806161098663, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:50<35:18, 4.15s/it] 2%|▏ | 11/520 [00:54<34:37, 4.08s/it] {'loss': 1.5041, 'grad_norm': 0.0008260048763770654, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:54<34:37, 4.08s/it] 2%|▏ | 12/520 [00:58<33:51, 4.00s/it] {'loss': 1.3606, 'grad_norm': 0.0006392844664679172, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:58<33:51, 4.00s/it][2025-10-17 20:53:05,472] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:02<35:02, 4.15s/it] {'loss': 1.4284, 'grad_norm': 0.000617021161892867, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:02<35:02, 4.15s/it] 3%|▎ | 14/520 [01:06<34:07, 4.05s/it] {'loss': 1.4699, 'grad_norm': 0.0005990072673760541, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:06<34:07, 4.05s/it] 3%|▎ | 15/520 [01:10<33:30, 3.98s/it] {'loss': 1.3899, 'grad_norm': 0.0004580761611584463, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:10<33:30, 3.98s/it] 3%|▎ | 16/520 [01:13<33:03, 3.94s/it] {'loss': 1.3622, 'grad_norm': 0.0005658781347323867, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:13<33:03, 3.94s/it] 3%|▎ | 17/520 [01:17<32:34, 3.89s/it] {'loss': 1.5003, 'grad_norm': 0.0006707433089228147, 'learning_rate': 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0.0006312112843284459, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:14<13:20, 3.78s/it] 59%|█████▉ | 309/520 [19:17<13:10, 3.75s/it] {'loss': 1.1712, 'grad_norm': 0.0005984771027880918, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:17<13:10, 3.75s/it] 60%|█████▉ | 310/520 [19:21<13:02, 3.73s/it] {'loss': 1.1469, 'grad_norm': 0.0006046205010442528, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:21<13:02, 3.73s/it] 60%|█████▉ | 311/520 [19:25<12:55, 3.71s/it] {'loss': 1.1308, 'grad_norm': 0.0006025406287838354, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:25<12:55, 3.71s/it] 60%|██████ | 312/520 [19:28<12:51, 3.71s/it] {'loss': 1.1218, 'grad_norm': 0.0006243291110474451, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:28<12:51, 3.71s/it] 60%|██████ | 313/520 [19:32<12:46, 3.70s/it] {'loss': 1.0999, 'grad_norm': 0.0005581837938035649, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:32<12:46, 3.70s/it] 60%|██████ | 314/520 [19:36<13:02, 3.80s/it] {'loss': 1.1408, 'grad_norm': 0.0005900216031248078, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:36<13:02, 3.80s/it] 61%|██████ | 315/520 [19:40<12:52, 3.77s/it] {'loss': 1.1794, 'grad_norm': 0.000709818339266603, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:40<12:52, 3.77s/it] 61%|██████ | 316/520 [19:44<13:11, 3.88s/it] {'loss': 1.126, 'grad_norm': 0.0006363489807088217, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:44<13:11, 3.88s/it] 61%|██████ | 317/520 [19:48<12:55, 3.82s/it] {'loss': 1.1281, 'grad_norm': 0.0005340853182711033, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:48<12:55, 3.82s/it] 61%|██████ | 318/520 [19:51<12:46, 3.79s/it] {'loss': 1.2365, 'grad_norm': 0.0006568279114763719, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:51<12:46, 3.79s/it] 61%|██████▏ | 319/520 [19:55<12:56, 3.87s/it] {'loss': 1.1223, 'grad_norm': 0.0005366294323417956, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:55<12:56, 3.87s/it] 62%|██████▏ | 320/520 [19:59<12:41, 3.81s/it] {'loss': 1.0642, 'grad_norm': 0.0006177623645249252, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:59<12:41, 3.81s/it] 62%|██████▏ | 321/520 [20:03<12:28, 3.76s/it] {'loss': 1.2593, 'grad_norm': 0.0006172678014369908, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:03<12:28, 3.76s/it] 62%|██████▏ | 322/520 [20:07<12:24, 3.76s/it] {'loss': 1.0826, 'grad_norm': 0.0005690879092844739, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:07<12:24, 3.76s/it] 62%|██████▏ | 323/520 [20:10<12:27, 3.79s/it] {'loss': 1.1529, 'grad_norm': 0.0005954896897506788, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:10<12:27, 3.79s/it] 62%|██████▏ | 324/520 [20:14<12:14, 3.75s/it] {'loss': 1.205, 'grad_norm': 0.0005999811837277627, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:14<12:14, 3.75s/it] 62%|██████▎ | 325/520 [20:18<12:03, 3.71s/it] {'loss': 1.201, 'grad_norm': 0.0006217738572923779, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:18<12:03, 3.71s/it] 63%|██████▎ | 326/520 [20:21<12:04, 3.73s/it] {'loss': 1.2026, 'grad_norm': 0.0006355402495188419, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:21<12:04, 3.73s/it] 63%|██████▎ | 327/520 [20:25<12:01, 3.74s/it] {'loss': 1.1803, 'grad_norm': 0.0006326236463279697, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:25<12:01, 3.74s/it] 63%|██████▎ | 328/520 [20:29<12:00, 3.75s/it] {'loss': 1.2393, 'grad_norm': 0.0006312958037183826, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:29<12:00, 3.75s/it] 63%|██████▎ | 329/520 [20:33<12:01, 3.78s/it] {'loss': 1.1252, 'grad_norm': 0.0005286800796379104, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:33<12:01, 3.78s/it] 63%|██████▎ | 330/520 [20:37<11:59, 3.79s/it] {'loss': 1.1981, 'grad_norm': 0.0005596285574248676, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:37<11:59, 3.79s/it] 64%|██████▎ | 331/520 [20:40<11:59, 3.81s/it] {'loss': 1.1589, 'grad_norm': 0.0006097692724647292, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:40<11:59, 3.81s/it] 64%|██████▍ | 332/520 [20:44<11:59, 3.83s/it] {'loss': 1.2118, 'grad_norm': 0.0005443804268751039, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:44<11:59, 3.83s/it] 64%|██████▍ | 333/520 [20:48<11:55, 3.82s/it] {'loss': 1.2911, 'grad_norm': 0.0006390639791009419, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:48<11:55, 3.82s/it] 64%|██████▍ | 334/520 [20:52<11:49, 3.81s/it] {'loss': 1.2043, 'grad_norm': 0.0006429833361771493, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:52<11:49, 3.81s/it] 64%|██████▍ | 335/520 [20:56<11:36, 3.77s/it] {'loss': 1.2046, 'grad_norm': 0.0005840274320115705, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:56<11:36, 3.77s/it] 65%|██████▍ | 336/520 [20:59<11:26, 3.73s/it] {'loss': 1.1135, 'grad_norm': 0.0006576680434261498, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:59<11:26, 3.73s/it] 65%|██████▍ | 337/520 [21:03<11:18, 3.71s/it] {'loss': 1.1056, 'grad_norm': 0.0005976851148927744, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:03<11:18, 3.71s/it] 65%|██████▌ | 338/520 [21:07<11:11, 3.69s/it] {'loss': 1.2099, 'grad_norm': 0.0005923172205492054, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:07<11:11, 3.69s/it] 65%|██████▌ | 339/520 [21:10<11:05, 3.68s/it] {'loss': 1.1549, 'grad_norm': 0.0006232079856054028, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:10<11:05, 3.68s/it] 65%|██████▌ | 340/520 [21:14<11:01, 3.67s/it] {'loss': 1.1422, 'grad_norm': 0.000584810291133204, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:14<11:01, 3.67s/it] 66%|██████▌ | 341/520 [21:18<10:55, 3.66s/it] {'loss': 1.1719, 'grad_norm': 0.0006377321176619434, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:18<10:55, 3.66s/it] 66%|██████▌ | 342/520 [21:21<10:50, 3.65s/it] {'loss': 1.1893, 'grad_norm': 0.0006970540411806841, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:21<10:50, 3.65s/it] 66%|██████▌ | 343/520 [21:25<10:46, 3.65s/it] {'loss': 1.1374, 'grad_norm': 0.0005297467445198777, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:25<10:46, 3.65s/it] 66%|██████▌ | 344/520 [21:28<10:43, 3.66s/it] {'loss': 1.1306, 'grad_norm': 0.0005566331493856199, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:28<10:43, 3.66s/it] 66%|██████▋ | 345/520 [21:32<10:42, 3.67s/it] {'loss': 1.2278, 'grad_norm': 0.0006010426070685343, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:32<10:42, 3.67s/it] 67%|██████▋ | 346/520 [21:36<10:37, 3.66s/it] {'loss': 1.1584, 'grad_norm': 0.0006003552730145889, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:36<10:37, 3.66s/it] 67%|██████▋ | 347/520 [21:39<10:32, 3.66s/it] {'loss': 1.1461, 'grad_norm': 0.0005436865297921286, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:39<10:32, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:43<10:30, 3.67s/it] {'loss': 1.1015, 'grad_norm': 0.000722557522266593, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:43<10:30, 3.67s/it] 67%|██████▋ | 349/520 [21:47<10:26, 3.66s/it] {'loss': 1.1391, 'grad_norm': 0.0005961852565156535, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:47<10:26, 3.66s/it] 67%|██████▋ | 350/520 [21:50<10:20, 3.65s/it] {'loss': 1.1837, 'grad_norm': 0.0006695014444263975, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:50<10:20, 3.65s/it] 68%|██████▊ | 351/520 [21:54<10:18, 3.66s/it] {'loss': 1.0955, 'grad_norm': 0.000566106634145277, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:54<10:18, 3.66s/it] 68%|██████▊ | 352/520 [21:58<10:13, 3.65s/it] {'loss': 1.2075, 'grad_norm': 0.0005618196503632706, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:58<10:13, 3.65s/it] 68%|██████▊ | 353/520 [22:01<10:13, 3.67s/it] {'loss': 1.1311, 'grad_norm': 0.0005006985667417639, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:01<10:13, 3.67s/it] 68%|██████▊ | 354/520 [22:05<10:07, 3.66s/it] {'loss': 1.2225, 'grad_norm': 0.0005643125702179185, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:05<10:07, 3.66s/it] 68%|██████▊ | 355/520 [22:09<10:04, 3.66s/it] {'loss': 1.156, 'grad_norm': 0.0005851773695237437, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:09<10:04, 3.66s/it] 68%|██████▊ | 356/520 [22:12<10:00, 3.66s/it] {'loss': 1.1565, 'grad_norm': 0.0006107239404820179, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:12<10:00, 3.66s/it] 69%|██████▊ | 357/520 [22:16<09:55, 3.65s/it] {'loss': 1.1923, 'grad_norm': 0.0005685957803624925, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:16<09:55, 3.65s/it] 69%|██████▉ | 358/520 [22:20<09:51, 3.65s/it] {'loss': 1.1191, 'grad_norm': 0.0005966505320456448, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:20<09:51, 3.65s/it] 69%|██████▉ | 359/520 [22:23<09:47, 3.65s/it] {'loss': 1.1664, 'grad_norm': 0.000595650409768072, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:23<09:47, 3.65s/it] 69%|██████▉ | 360/520 [22:27<09:44, 3.65s/it] {'loss': 1.1715, 'grad_norm': 0.000624964451289584, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:27<09:44, 3.65s/it] 69%|██████▉ | 361/520 [22:31<09:40, 3.65s/it] {'loss': 1.1905, 'grad_norm': 0.0005167697745409985, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:31<09:40, 3.65s/it] 70%|██████▉ | 362/520 [22:34<09:36, 3.65s/it] {'loss': 1.1676, 'grad_norm': 0.0006425938617781199, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:34<09:36, 3.65s/it] 70%|██████▉ | 363/520 [22:38<09:32, 3.64s/it] {'loss': 1.1967, 'grad_norm': 0.0005911751166910234, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:38<09:32, 3.64s/it] 70%|███████ | 364/520 [22:42<09:28, 3.64s/it] {'loss': 1.2034, 'grad_norm': 0.0005869093891097411, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:42<09:28, 3.64s/it] 70%|███████ | 365/520 [22:45<09:24, 3.64s/it] {'loss': 1.2476, 'grad_norm': 0.0006108603622287808, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:45<09:24, 3.64s/it] 70%|███████ | 366/520 [22:49<09:22, 3.65s/it] {'loss': 1.2133, 'grad_norm': 0.000570007745300405, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:49<09:22, 3.65s/it] 71%|███████ | 367/520 [22:53<09:18, 3.65s/it] {'loss': 1.2122, 'grad_norm': 0.0006109509873993475, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:53<09:18, 3.65s/it] 71%|███████ | 368/520 [22:56<09:14, 3.65s/it] {'loss': 1.0665, 'grad_norm': 0.0005970949296463962, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:56<09:14, 3.65s/it] 71%|███████ | 369/520 [23:00<09:10, 3.64s/it] {'loss': 1.1652, 'grad_norm': 0.0005313091422930157, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:00<09:10, 3.64s/it] 71%|███████ | 370/520 [23:03<09:05, 3.64s/it] {'loss': 1.1269, 'grad_norm': 0.0005621300775306376, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:03<09:05, 3.64s/it] 71%|███████▏ | 371/520 [23:07<09:00, 3.63s/it] {'loss': 1.1197, 'grad_norm': 0.0006247272391440921, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:07<09:00, 3.63s/it] 72%|███████▏ | 372/520 [23:11<08:57, 3.63s/it] {'loss': 1.2309, 'grad_norm': 0.0005703603942666479, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:11<08:57, 3.63s/it] 72%|███████▏ | 373/520 [23:14<08:55, 3.64s/it] {'loss': 1.1218, 'grad_norm': 0.000624845828302691, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:14<08:55, 3.64s/it] 72%|███████▏ | 374/520 [23:18<08:50, 3.63s/it] {'loss': 1.2138, 'grad_norm': 0.0006193873935337828, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:18<08:50, 3.63s/it] 72%|███████▏ | 375/520 [23:22<08:46, 3.63s/it] {'loss': 1.1285, 'grad_norm': 0.0006057566735472577, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:22<08:46, 3.63s/it] 72%|███████▏ | 376/520 [23:25<08:42, 3.63s/it] {'loss': 1.2356, 'grad_norm': 0.0005723091943298877, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:25<08:42, 3.63s/it] 72%|███████▎ | 377/520 [23:29<08:39, 3.64s/it] {'loss': 1.1654, 'grad_norm': 0.0006337438916370776, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:29<08:39, 3.64s/it] 73%|███████▎ | 378/520 [23:33<08:38, 3.65s/it] {'loss': 1.2292, 'grad_norm': 0.0005710304635371939, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:33<08:38, 3.65s/it] 73%|███████▎ | 379/520 [23:36<08:33, 3.64s/it] {'loss': 1.1965, 'grad_norm': 0.0005624971305407194, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:36<08:33, 3.64s/it] 73%|███████▎ | 380/520 [23:40<08:29, 3.64s/it] {'loss': 1.2108, 'grad_norm': 0.0005870772243789882, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:40<08:29, 3.64s/it] 73%|███████▎ | 381/520 [23:43<08:25, 3.63s/it] {'loss': 1.2056, 'grad_norm': 0.0005619573274443805, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:43<08:25, 3.63s/it] 73%|███████▎ | 382/520 [23:47<08:22, 3.64s/it] {'loss': 1.1818, 'grad_norm': 0.0005487156041072151, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:47<08:22, 3.64s/it] 74%|███████▎ | 383/520 [23:51<08:17, 3.63s/it] {'loss': 1.0487, 'grad_norm': 0.0006679942071066661, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:51<08:17, 3.63s/it] 74%|███████▍ | 384/520 [23:54<08:14, 3.63s/it] {'loss': 1.2036, 'grad_norm': 0.000532076602408408, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:54<08:14, 3.63s/it] 74%|███████▍ | 385/520 [23:58<08:09, 3.63s/it] {'loss': 1.1906, 'grad_norm': 0.0005488639449475454, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:58<08:09, 3.63s/it] 74%|███████▍ | 386/520 [24:02<08:05, 3.62s/it] {'loss': 1.1425, 'grad_norm': 0.0005180615033489615, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:02<08:05, 3.62s/it] 74%|███████▍ | 387/520 [24:05<08:01, 3.62s/it] {'loss': 1.2305, 'grad_norm': 0.0005673879352518204, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:05<08:01, 3.62s/it] 75%|███████▍ | 388/520 [24:09<07:58, 3.63s/it] {'loss': 1.1033, 'grad_norm': 0.0005700384767031058, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:09<07:58, 3.63s/it] 75%|███████▍ | 389/520 [24:12<07:55, 3.63s/it] {'loss': 1.1493, 'grad_norm': 0.0007470179464554679, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:12<07:55, 3.63s/it] 75%|███████▌ | 390/520 [24:16<07:52, 3.63s/it] {'loss': 1.2161, 'grad_norm': 0.0005750623021011895, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:16<07:52, 3.63s/it] 75%|███████▌ | 391/520 [24:20<07:48, 3.63s/it] {'loss': 1.2753, 'grad_norm': 0.0006095822376291203, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:20<07:48, 3.63s/it] 75%|███████▌ | 392/520 [24:23<07:46, 3.65s/it] {'loss': 1.1018, 'grad_norm': 0.000582242235638863, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:23<07:46, 3.65s/it] 76%|███████▌ | 393/520 [24:27<07:42, 3.64s/it] {'loss': 1.0911, 'grad_norm': 0.0004933657781272185, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:27<07:42, 3.64s/it] 76%|███████▌ | 394/520 [24:31<07:38, 3.64s/it] {'loss': 1.1719, 'grad_norm': 0.0006293783236396482, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:31<07:38, 3.64s/it] 76%|███████▌ | 395/520 [24:34<07:34, 3.64s/it] {'loss': 1.1373, 'grad_norm': 0.0006282150100156043, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:34<07:34, 3.64s/it] 76%|███████▌ | 396/520 [24:38<07:30, 3.63s/it] {'loss': 1.2151, 'grad_norm': 0.0006371074656721334, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:38<07:30, 3.63s/it] 76%|███████▋ | 397/520 [24:42<07:28, 3.65s/it] {'loss': 1.1895, 'grad_norm': 0.0005733318763220242, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:42<07:28, 3.65s/it] 77%|███████▋ | 398/520 [24:45<07:25, 3.66s/it] {'loss': 1.1869, 'grad_norm': 0.0006294515166038823, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:45<07:25, 3.66s/it] 77%|███████▋ | 399/520 [24:49<07:22, 3.66s/it] {'loss': 1.1249, 'grad_norm': 0.000558387211326119, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:49<07:22, 3.66s/it] 77%|███████▋ | 400/520 [24:53<07:19, 3.66s/it] {'loss': 1.1564, 'grad_norm': 0.000538036049382514, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:53<07:19, 3.66s/it] 77%|███████▋ | 401/520 [24:56<07:15, 3.66s/it] {'loss': 1.026, 'grad_norm': 0.0006536679393304254, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:56<07:15, 3.66s/it] 77%|███████▋ | 402/520 [25:00<07:10, 3.65s/it] {'loss': 1.1556, 'grad_norm': 0.0006061081313132765, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:00<07:10, 3.65s/it] 78%|███████▊ | 403/520 [25:04<07:07, 3.65s/it] {'loss': 1.1756, 'grad_norm': 0.0006415408193215531, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:04<07:07, 3.65s/it] 78%|███████▊ | 404/520 [25:07<07:04, 3.66s/it] {'loss': 1.0887, 'grad_norm': 0.0006802414727977901, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:07<07:04, 3.66s/it] 78%|███████▊ | 405/520 [25:11<07:06, 3.71s/it] {'loss': 1.1387, 'grad_norm': 0.0005839639374826832, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:11<07:06, 3.71s/it] 78%|███████▊ | 406/520 [25:15<07:06, 3.75s/it] {'loss': 1.059, 'grad_norm': 0.0007317763502619306, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:15<07:06, 3.75s/it] 78%|███████▊ | 407/520 [25:19<07:04, 3.76s/it] {'loss': 1.2544, 'grad_norm': 0.0006331841532428232, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:19<07:04, 3.76s/it] 78%|███████▊ | 408/520 [25:22<07:02, 3.77s/it] {'loss': 1.1707, 'grad_norm': 0.0007148217336107369, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:22<07:02, 3.77s/it] 79%|███████▊ | 409/520 [25:26<06:59, 3.78s/it] {'loss': 1.2826, 'grad_norm': 0.0006647935093236039, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:26<06:59, 3.78s/it] 79%|███████▉ | 410/520 [25:30<06:56, 3.79s/it] {'loss': 1.029, 'grad_norm': 0.0005989668475828152, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:30<06:56, 3.79s/it] 79%|███████▉ | 411/520 [25:34<06:53, 3.80s/it] {'loss': 1.2647, 'grad_norm': 0.0006316199609646467, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:34<06:53, 3.80s/it] 79%|███████▉ | 412/520 [25:37<06:44, 3.75s/it] {'loss': 1.1745, 'grad_norm': 0.000587250476559212, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:37<06:44, 3.75s/it] 79%|███████▉ | 413/520 [25:41<06:37, 3.72s/it] {'loss': 1.153, 'grad_norm': 0.0005673933845610134, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:41<06:37, 3.72s/it] 80%|███████▉ | 414/520 [25:45<06:32, 3.70s/it] {'loss': 0.9653, 'grad_norm': 0.0004943010552627671, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:45<06:32, 3.70s/it] 80%|███████▉ | 415/520 [25:48<06:27, 3.69s/it] {'loss': 1.1594, 'grad_norm': 0.0005663989198470175, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:48<06:27, 3.69s/it] 80%|████████ | 416/520 [25:52<06:22, 3.68s/it] {'loss': 1.0619, 'grad_norm': 0.0006770254265334052, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:52<06:22, 3.68s/it] 80%|████████ | 417/520 [25:56<06:17, 3.66s/it] {'loss': 1.2258, 'grad_norm': 0.0005922691522553319, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:56<06:17, 3.66s/it] 80%|████████ | 418/520 [25:59<06:13, 3.66s/it] {'loss': 1.2186, 'grad_norm': 0.0005704770432518929, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:59<06:13, 3.66s/it] 81%|████████ | 419/520 [26:03<06:09, 3.66s/it] {'loss': 1.2119, 'grad_norm': 0.0006459820268353614, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:03<06:09, 3.66s/it] 81%|████████ | 420/520 [26:07<06:05, 3.66s/it] {'loss': 1.1026, 'grad_norm': 0.0006148647562776604, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:07<06:05, 3.66s/it] 81%|████████ | 421/520 [26:10<06:01, 3.65s/it] {'loss': 1.0414, 'grad_norm': 0.0006497170722286792, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:10<06:01, 3.65s/it] 81%|████████ | 422/520 [26:14<05:58, 3.66s/it] {'loss': 1.1628, 'grad_norm': 0.0006280249135973786, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:14<05:58, 3.66s/it] 81%|████████▏ | 423/520 [26:18<05:54, 3.66s/it] {'loss': 1.1291, 'grad_norm': 0.0006381996172812426, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:18<05:54, 3.66s/it] 82%|████████▏ | 424/520 [26:21<05:53, 3.68s/it] {'loss': 1.2373, 'grad_norm': 0.0005573811625290302, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:21<05:53, 3.68s/it] 82%|████████▏ | 425/520 [26:25<05:49, 3.68s/it] {'loss': 1.1484, 'grad_norm': 0.0005920582018030935, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:25<05:49, 3.68s/it] 82%|████████▏ | 426/520 [26:29<05:44, 3.66s/it] {'loss': 1.1801, 'grad_norm': 0.0007928458704554867, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:29<05:44, 3.66s/it] 82%|████████▏ | 427/520 [26:32<05:39, 3.65s/it] {'loss': 1.0817, 'grad_norm': 0.0005700376865400954, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:32<05:39, 3.65s/it] 82%|████████▏ | 428/520 [26:36<05:34, 3.63s/it] {'loss': 1.0742, 'grad_norm': 0.0006413473696885616, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:36<05:34, 3.63s/it] 82%|████████▎ | 429/520 [26:40<05:31, 3.64s/it] {'loss': 1.1692, 'grad_norm': 0.0006212798879911024, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:40<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:43<05:27, 3.64s/it] {'loss': 1.1715, 'grad_norm': 0.0005619578180294634, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:43<05:27, 3.64s/it] 83%|████████▎ | 431/520 [26:47<05:23, 3.64s/it] {'loss': 1.1255, 'grad_norm': 0.0006060503849813918, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:47<05:23, 3.64s/it] 83%|████████▎ | 432/520 [26:50<05:20, 3.64s/it] {'loss': 1.0784, 'grad_norm': 0.0006147555546672879, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:51<05:20, 3.64s/it] 83%|████████▎ | 433/520 [26:54<05:18, 3.66s/it] {'loss': 1.2103, 'grad_norm': 0.0006008809333965849, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:54<05:18, 3.66s/it] 83%|████████▎ | 434/520 [26:58<05:14, 3.65s/it] {'loss': 0.9636, 'grad_norm': 0.0006085627511034194, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:58<05:14, 3.65s/it] 84%|████████▎ | 435/520 [27:01<05:09, 3.65s/it] {'loss': 1.2409, 'grad_norm': 0.0006532951274322237, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:01<05:09, 3.65s/it] 84%|████████▍ | 436/520 [27:05<05:06, 3.64s/it] {'loss': 1.0526, 'grad_norm': 0.0006242191671009378, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:05<05:06, 3.64s/it] 84%|████████▍ | 437/520 [27:09<05:02, 3.64s/it] {'loss': 1.2628, 'grad_norm': 0.0005998387512500966, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:09<05:02, 3.64s/it] 84%|████████▍ | 438/520 [27:12<04:59, 3.65s/it] {'loss': 1.0877, 'grad_norm': 0.0006068707268672148, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:12<04:59, 3.65s/it] 84%|████████▍ | 439/520 [27:16<04:56, 3.65s/it] {'loss': 1.1092, 'grad_norm': 0.00047857041955548497, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:16<04:56, 3.65s/it] 85%|████████▍ | 440/520 [27:20<04:51, 3.65s/it] {'loss': 1.1213, 'grad_norm': 0.0006538506276177759, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:20<04:51, 3.65s/it] 85%|████████▍ | 441/520 [27:23<04:49, 3.66s/it] {'loss': 1.1225, 'grad_norm': 0.00067145335389263, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:23<04:49, 3.66s/it] 85%|████████▌ | 442/520 [27:27<04:45, 3.65s/it] {'loss': 1.1826, 'grad_norm': 0.0006570644050046199, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:27<04:45, 3.65s/it] 85%|████████▌ | 443/520 [27:31<04:40, 3.64s/it] {'loss': 1.1932, 'grad_norm': 0.0005775834078026924, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:31<04:40, 3.64s/it] 85%|████████▌ | 444/520 [27:34<04:37, 3.65s/it] {'loss': 1.1594, 'grad_norm': 0.0005330934622037979, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:34<04:37, 3.65s/it] 86%|████████▌ | 445/520 [27:38<04:32, 3.63s/it] {'loss': 1.0868, 'grad_norm': 0.0005715699770353379, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:38<04:32, 3.63s/it] 86%|████████▌ | 446/520 [27:42<04:29, 3.64s/it] {'loss': 1.1986, 'grad_norm': 0.0005262711217721271, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:42<04:29, 3.64s/it] 86%|████████▌ | 447/520 [27:45<04:26, 3.66s/it] {'loss': 1.1565, 'grad_norm': 0.0005737626119469041, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:45<04:26, 3.66s/it] 86%|████████▌ | 448/520 [27:49<04:22, 3.65s/it] {'loss': 1.1597, 'grad_norm': 0.000656310422883852, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:49<04:22, 3.65s/it] 86%|████████▋ | 449/520 [27:53<04:18, 3.64s/it] {'loss': 1.158, 'grad_norm': 0.0005781810272337059, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:53<04:18, 3.64s/it] 87%|████████▋ | 450/520 [27:56<04:14, 3.63s/it] {'loss': 1.1807, 'grad_norm': 0.000598330101531573, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:56<04:14, 3.63s/it] 87%|████████▋ | 451/520 [28:00<04:11, 3.64s/it] {'loss': 1.1859, 'grad_norm': 0.0006118181391321343, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:00<04:11, 3.64s/it] 87%|████████▋ | 452/520 [28:03<04:08, 3.65s/it] {'loss': 1.2049, 'grad_norm': 0.0005544287626921877, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:03<04:08, 3.65s/it] 87%|████████▋ | 453/520 [28:07<04:04, 3.65s/it] {'loss': 1.1811, 'grad_norm': 0.0005640168313654068, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:07<04:04, 3.65s/it] 87%|████████▋ | 454/520 [28:11<04:01, 3.65s/it] {'loss': 1.0919, 'grad_norm': 0.000608596022419614, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:11<04:01, 3.65s/it] 88%|████████▊ | 455/520 [28:14<03:58, 3.66s/it] {'loss': 1.2311, 'grad_norm': 0.0005901810152611768, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:14<03:58, 3.66s/it] 88%|████████▊ | 456/520 [28:18<03:54, 3.66s/it] {'loss': 1.1658, 'grad_norm': 0.0006164842325445675, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:18<03:54, 3.66s/it] 88%|████████▊ | 457/520 [28:22<03:50, 3.65s/it] {'loss': 1.0685, 'grad_norm': 0.0005023023668613611, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:22<03:50, 3.65s/it] 88%|████████▊ | 458/520 [28:25<03:47, 3.67s/it] {'loss': 1.2821, 'grad_norm': 0.0006495533554901351, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:25<03:47, 3.67s/it] 88%|████████▊ | 459/520 [28:29<03:43, 3.66s/it] {'loss': 1.215, 'grad_norm': 0.0006076224473095039, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:29<03:43, 3.66s/it] 88%|████████▊ | 460/520 [28:33<03:39, 3.66s/it] {'loss': 1.1087, 'grad_norm': 0.0005776317325985573, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:33<03:39, 3.66s/it] 89%|████████▊ | 461/520 [28:36<03:35, 3.66s/it] {'loss': 1.1488, 'grad_norm': 0.0004465028569804267, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:36<03:35, 3.66s/it] 89%|████████▉ | 462/520 [28:40<03:32, 3.66s/it] {'loss': 1.2471, 'grad_norm': 0.000556690956543051, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:40<03:32, 3.66s/it] 89%|████████▉ | 463/520 [28:44<03:28, 3.66s/it] {'loss': 1.0794, 'grad_norm': 0.0006310687747191067, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:44<03:28, 3.66s/it] 89%|████████▉ | 464/520 [28:47<03:24, 3.65s/it] {'loss': 1.1988, 'grad_norm': 0.000610676856218101, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:47<03:24, 3.65s/it] 89%|████████▉ | 465/520 [28:51<03:20, 3.65s/it] {'loss': 1.2997, 'grad_norm': 0.000614783424545834, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:51<03:20, 3.65s/it] 90%|████████▉ | 466/520 [28:55<03:17, 3.65s/it] {'loss': 1.1948, 'grad_norm': 0.0005599155437007592, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:55<03:17, 3.65s/it] 90%|████████▉ | 467/520 [28:58<03:13, 3.65s/it] {'loss': 1.136, 'grad_norm': 0.0005395579652108255, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:58<03:13, 3.65s/it] 90%|█████████ | 468/520 [29:02<03:09, 3.65s/it] {'loss': 1.1597, 'grad_norm': 0.0006728120184095963, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:02<03:09, 3.65s/it] 90%|█████████ | 469/520 [29:06<03:06, 3.65s/it] {'loss': 1.2317, 'grad_norm': 0.0007044891620080955, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:06<03:06, 3.65s/it] 90%|█████████ | 470/520 [29:09<03:02, 3.65s/it] {'loss': 1.1048, 'grad_norm': 0.0005518678976535506, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:09<03:02, 3.65s/it] 91%|█████████ | 471/520 [29:13<02:58, 3.64s/it] {'loss': 1.1318, 'grad_norm': 0.000639513452359059, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:13<02:58, 3.64s/it] 91%|█████████ | 472/520 [29:17<02:55, 3.66s/it] {'loss': 1.1021, 'grad_norm': 0.0006121651669451525, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:17<02:55, 3.66s/it] 91%|█████████ | 473/520 [29:20<02:52, 3.67s/it] {'loss': 1.1706, 'grad_norm': 0.0006179265399644676, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:20<02:52, 3.67s/it] 91%|█████████ | 474/520 [29:24<02:48, 3.66s/it] {'loss': 1.1706, 'grad_norm': 0.000549742572051187, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:24<02:48, 3.66s/it] 91%|█████████▏| 475/520 [29:28<02:44, 3.66s/it] {'loss': 1.0873, 'grad_norm': 0.0005501707374747514, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:28<02:44, 3.66s/it] 92%|█████████▏| 476/520 [29:31<02:40, 3.65s/it] {'loss': 1.153, 'grad_norm': 0.0006160389867340245, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:31<02:40, 3.65s/it] 92%|█████████▏| 477/520 [29:35<02:36, 3.65s/it] {'loss': 1.1521, 'grad_norm': 0.000673710361598721, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:35<02:36, 3.65s/it] 92%|█████████▏| 478/520 [29:38<02:33, 3.64s/it] {'loss': 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{'loss': 1.2709, 'grad_norm': 0.0005350712472242532, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:24<00:47, 3.64s/it] 98%|█████████▊| 508/520 [31:28<00:44, 3.71s/it] {'loss': 1.2528, 'grad_norm': 0.0006067477655665534, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:28<00:44, 3.71s/it] 98%|█████████▊| 509/520 [31:32<00:41, 3.76s/it] {'loss': 1.224, 'grad_norm': 0.0005887456737395262, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:32<00:41, 3.76s/it] 98%|█████████▊| 510/520 [31:36<00:38, 3.80s/it] {'loss': 1.1734, 'grad_norm': 0.000598478016714858, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:36<00:38, 3.80s/it] 98%|█████████▊| 511/520 [31:40<00:34, 3.83s/it] {'loss': 1.1378, 'grad_norm': 0.0005761046564271836, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:40<00:34, 3.83s/it] 98%|█████████▊| 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[31:59<00:15, 3.78s/it] 99%|█████████▉| 517/520 [32:02<00:11, 3.78s/it] {'loss': 1.1686, 'grad_norm': 0.0005553436161836056, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:02<00:11, 3.78s/it] 100%|█████████▉| 518/520 [32:06<00:07, 3.78s/it] {'loss': 1.1646, 'grad_norm': 0.0006151641657497258, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:06<00:07, 3.78s/it] 100%|█████████▉| 519/520 [32:10<00:03, 3.79s/it] {'loss': 1.1426, 'grad_norm': 0.0005689868475911055, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:10<00:03, 3.79s/it] 100%|██████████| 520/520 [32:15<00:00, 4.01s/it] {'loss': 1.1319, 'grad_norm': 0.0005277457221401849, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:15<00:00, 4.01s/it] {'train_runtime': 1935.1321, 'train_samples_per_second': 34.38, 'train_steps_per_second': 0.269, 'train_loss': 1.2203925453699551, 'epoch': 1.0} + 100%|██████████| 520/520 [32:15<00:00, 4.01s/it] 100%|██████████| 520/520 [32:15<00:00, 3.72s/it] +[2025-10-17 21:24:24,926] [INFO] [launch.py:348:main] Process 657661 exits successfully. +[2025-10-17 21:24:24,926] [INFO] [launch.py:348:main] Process 657657 exits successfully. +[2025-10-17 21:24:24,927] [INFO] [launch.py:348:main] Process 657656 exits successfully. +[2025-10-17 21:24:24,927] [INFO] [launch.py:348:main] Process 657658 exits successfully. +[2025-10-17 21:24:24,928] [INFO] [launch.py:348:main] Process 657660 exits successfully. +[2025-10-17 21:24:25,929] [INFO] [launch.py:348:main] Process 657659 exits successfully. +[2025-10-17 21:24:25,930] [INFO] [launch.py:348:main] Process 657662 exits successfully. +[2025-10-17 21:24:27,932] [INFO] [launch.py:348:main] Process 657655 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.7_2e-1_connector-7.0_1.7_2e-1_ablation_20251017_205037.log +Timestamp: 2025-10-17 21:24:30 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation_20251017_212430.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation_20251017_212430.log new file mode 100644 index 0000000000000000000000000000000000000000..8d453cf86e10fadbb8096d26ef8aa2134e9f746d --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation_20251017_212430.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation_20251017_212430.log +Timestamp: 2025-10-17 21:24:30 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:24:33,024] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:35,708] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 21:24:35,709] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 1.9 --temperature_mlp_text 1.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 1.9 --temperature_mlp_vision 1.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 1.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:24:38,260] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:39,353] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 21:24:39,353] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 21:24:39,353] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 21:24:39,353] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 21:24:39,353] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 21:24:39,353] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 21:24:39,353] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 21:24:39,355] [INFO] [launch.py:253:main] process 679777 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,357] [INFO] [launch.py:253:main] process 679778 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,359] [INFO] [launch.py:253:main] process 679779 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,361] [INFO] [launch.py:253:main] process 679780 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,363] [INFO] [launch.py:253:main] process 679781 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,364] [INFO] [launch.py:253:main] process 679782 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,366] [INFO] [launch.py:253:main] process 679783 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:24:39,368] [INFO] [launch.py:253:main] process 679784 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:24:46,054] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,358] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,376] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,423] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,439] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,453] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,460] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,471] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,471] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 21:24:46,477] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:24:46,791] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,803] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,850] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,865] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,877] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,890] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:24:46,895] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.9, 'temperature_mlp': 1.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.9, + "temperature_mlp": 1.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:679777:679777 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Using network Socket +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679783:679783 [6] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679779:679779 [2] NCCL INFO NET/Plugin: Using internal network plugin. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Using network Socket +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Using network Socket +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679782:679782 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679778:679778 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:679784:679784 [7] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679780:681376 [3] NCCL INFO ncclCommInitRank comm 0x55a8d640b780 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679778:681364 [1] NCCL INFO ncclCommInitRank comm 0x55af0ffe0800 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679784:681374 [7] NCCL INFO ncclCommInitRank comm 0x55f47e4b7400 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679779:681354 [2] NCCL INFO ncclCommInitRank comm 0x55c4000876a0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:679781:681375 [4] NCCL INFO ncclCommInitRank comm 0x55d310f92030 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679782:681355 [5] NCCL INFO ncclCommInitRank comm 0x55ca6728eea0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679783:681353 [6] NCCL INFO ncclCommInitRank comm 0x55edfe334f80 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x4978210eb1fc25a - Init COMPLETE +ywang29-vrdb-test1-worker-0:679777:681336 [0] NCCL INFO ncclCommInitRank comm 0x5591c5e51770 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x4978210eb1fc25a - Init COMPLETE +[2025-10-17 21:25:30,580] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 21:25:32,307] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 21:25:50,503 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 21:25:50,513 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679777:686283 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679779:686285 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679778:686288 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679780:686284 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679784:686290 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679781:686286 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679783:686287 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:679782:686289 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:30<1:09:50, 8.11s/it] 1%| | 4/520 [00:34<54:58, 6.39s/it] {'loss': 2.0676, 'grad_norm': 0.0075663521275877235, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:34<54:58, 6.39s/it] 1%| | 5/520 [00:37<46:25, 5.41s/it] {'loss': 1.789, 'grad_norm': 0.004370617558498938, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:37<46:25, 5.41s/it] 1%| | 6/520 [00:41<41:38, 4.86s/it] {'loss': 1.4346, 'grad_norm': 0.001545897908453811, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:41<41:38, 4.86s/it] 1%|▏ | 7/520 [00:45<38:26, 4.50s/it] {'loss': 1.5255, 'grad_norm': 0.001836677496040108, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:45<38:26, 4.50s/it] 2%|▏ | 8/520 [00:49<38:02, 4.46s/it] {'loss': 1.5325, 'grad_norm': 0.0012143527139440787, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:49<38:02, 4.46s/it] 2%|▏ | 9/520 [00:54<37:39, 4.42s/it] {'loss': 1.5636, 'grad_norm': 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If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:10<34:55, 4.13s/it] {'loss': 1.398, 'grad_norm': 0.0006960735363897218, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:10<34:55, 4.13s/it] 3%|▎ | 14/520 [01:13<33:52, 4.02s/it] {'loss': 1.4419, 'grad_norm': 0.0007943217922973164, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:13<33:52, 4.02s/it] 3%|▎ | 15/520 [01:17<33:09, 3.94s/it] {'loss': 1.3754, 'grad_norm': 0.0006777633027080197, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:17<33:09, 3.94s/it] 3%|▎ | 16/520 [01:21<32:45, 3.90s/it] {'loss': 1.3435, 'grad_norm': 0.0007844555180648066, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:21<32:45, 3.90s/it] 3%|▎ | 17/520 [01:25<32:22, 3.86s/it] {'loss': 1.4682, 'grad_norm': 0.0007936703507363798, 'learning_rate': 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3.64s/it] {'loss': 1.0988, 'grad_norm': 0.0006149876605912179, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:42<12:34, 3.64s/it] 60%|██████ | 314/520 [19:46<12:52, 3.75s/it] {'loss': 1.1377, 'grad_norm': 0.000603338803672632, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:46<12:52, 3.75s/it] 61%|██████ | 315/520 [19:49<12:41, 3.71s/it] {'loss': 1.1777, 'grad_norm': 0.0008342405047016405, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:49<12:41, 3.71s/it] 61%|██████ | 316/520 [19:53<12:58, 3.82s/it] {'loss': 1.1239, 'grad_norm': 0.0006756198082551585, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:53<12:58, 3.82s/it] 61%|██████ | 317/520 [19:57<12:41, 3.75s/it] {'loss': 1.1269, 'grad_norm': 0.0005532847407855926, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:57<12:41, 3.75s/it] 61%|██████ | 318/520 [20:00<12:28, 3.70s/it] {'loss': 1.2363, 'grad_norm': 0.0006674447116655786, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [20:00<12:28, 3.70s/it] 61%|██████▏ | 319/520 [20:04<12:40, 3.78s/it] {'loss': 1.1184, 'grad_norm': 0.0005534701369243011, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:04<12:40, 3.78s/it] 62%|██████▏ | 320/520 [20:08<12:26, 3.73s/it] {'loss': 1.0627, 'grad_norm': 0.0006644300762521252, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:08<12:26, 3.73s/it] 62%|██████▏ | 321/520 [20:12<12:15, 3.70s/it] {'loss': 1.257, 'grad_norm': 0.0006391961564274349, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:12<12:15, 3.70s/it] 62%|██████▏ | 322/520 [20:15<12:04, 3.66s/it] {'loss': 1.0827, 'grad_norm': 0.000603287584872153, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:15<12:04, 3.66s/it] 62%|██████▏ | 323/520 [20:19<12:03, 3.67s/it] {'loss': 1.1512, 'grad_norm': 0.0006121821392637539, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:19<12:03, 3.67s/it] 62%|██████▏ | 324/520 [20:23<12:07, 3.71s/it] {'loss': 1.2039, 'grad_norm': 0.0006214787871507701, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:23<12:07, 3.71s/it] 62%|██████▎ | 325/520 [20:27<12:10, 3.74s/it] {'loss': 1.1995, 'grad_norm': 0.0006640385922524151, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:27<12:10, 3.74s/it] 63%|██████▎ | 326/520 [20:30<12:10, 3.77s/it] {'loss': 1.2001, 'grad_norm': 0.000655430630760599, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:30<12:10, 3.77s/it] 63%|██████▎ | 327/520 [20:34<11:59, 3.73s/it] {'loss': 1.1798, 'grad_norm': 0.0006270532109237755, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:34<11:59, 3.73s/it] 63%|██████▎ | 328/520 [20:38<11:46, 3.68s/it] {'loss': 1.2383, 'grad_norm': 0.0006659077805424724, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:38<11:46, 3.68s/it] 63%|██████▎ | 329/520 [20:41<11:39, 3.66s/it] {'loss': 1.1234, 'grad_norm': 0.0005458496600804897, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:41<11:39, 3.66s/it] 63%|██████▎ | 330/520 [20:45<11:32, 3.65s/it] {'loss': 1.1966, 'grad_norm': 0.0005821552649199456, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:45<11:32, 3.65s/it] 64%|██████▎ | 331/520 [20:48<11:28, 3.64s/it] {'loss': 1.1587, 'grad_norm': 0.0006355627589615235, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:48<11:28, 3.64s/it] 64%|██████▍ | 332/520 [20:52<11:22, 3.63s/it] {'loss': 1.2112, 'grad_norm': 0.0005602950836823906, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:52<11:22, 3.63s/it] 64%|██████▍ | 333/520 [20:56<11:16, 3.62s/it] {'loss': 1.2903, 'grad_norm': 0.0006683815648254765, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:56<11:16, 3.62s/it] 64%|██████▍ | 334/520 [20:59<11:12, 3.62s/it] {'loss': 1.2012, 'grad_norm': 0.0006718441885468551, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:59<11:12, 3.62s/it] 64%|██████▍ | 335/520 [21:03<11:08, 3.61s/it] {'loss': 1.202, 'grad_norm': 0.0006127181104425377, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:03<11:08, 3.61s/it] 65%|██████▍ | 336/520 [21:06<11:04, 3.61s/it] {'loss': 1.109, 'grad_norm': 0.0006716667575400219, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:07<11:04, 3.61s/it] 65%|██████▍ | 337/520 [21:10<11:00, 3.61s/it] {'loss': 1.1013, 'grad_norm': 0.0006196653067831161, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:10<11:00, 3.61s/it] 65%|██████▌ | 338/520 [21:14<10:55, 3.60s/it] {'loss': 1.2069, 'grad_norm': 0.0006076996851135069, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:14<10:55, 3.60s/it] 65%|██████▌ | 339/520 [21:17<10:51, 3.60s/it] {'loss': 1.1524, 'grad_norm': 0.000654238941964701, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:17<10:51, 3.60s/it] 65%|██████▌ | 340/520 [21:21<10:48, 3.60s/it] {'loss': 1.1399, 'grad_norm': 0.0006099746046594749, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:21<10:48, 3.60s/it] 66%|██████▌ | 341/520 [21:25<10:49, 3.63s/it] {'loss': 1.1685, 'grad_norm': 0.000661023973566214, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:25<10:49, 3.63s/it] 66%|██████▌ | 342/520 [21:28<10:46, 3.63s/it] {'loss': 1.1862, 'grad_norm': 0.0007359033082094832, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:28<10:46, 3.63s/it] 66%|██████▌ | 343/520 [21:32<10:42, 3.63s/it] {'loss': 1.1376, 'grad_norm': 0.0005945336239456066, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:32<10:42, 3.63s/it] 66%|██████▌ | 344/520 [21:35<10:37, 3.62s/it] {'loss': 1.1276, 'grad_norm': 0.000590459424118077, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:35<10:37, 3.62s/it] 66%|██████▋ | 345/520 [21:39<10:37, 3.64s/it] {'loss': 1.2263, 'grad_norm': 0.0006296576145390476, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:39<10:37, 3.64s/it] 67%|██████▋ | 346/520 [21:43<10:38, 3.67s/it] {'loss': 1.1597, 'grad_norm': 0.0006759236263174912, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:43<10:38, 3.67s/it] 67%|██████▋ | 347/520 [21:47<10:43, 3.72s/it] {'loss': 1.1434, 'grad_norm': 0.0005650172478502366, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:47<10:43, 3.72s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:51<10:46, 3.76s/it] {'loss': 1.1002, 'grad_norm': 0.0008147300561014719, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:51<10:46, 3.76s/it] 67%|██████▋ | 349/520 [21:54<10:46, 3.78s/it] {'loss': 1.1369, 'grad_norm': 0.0006252850408356979, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:54<10:46, 3.78s/it] 67%|██████▋ | 350/520 [21:58<10:45, 3.79s/it] {'loss': 1.1818, 'grad_norm': 0.0007186833282213933, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:58<10:45, 3.79s/it] 68%|██████▊ | 351/520 [22:02<10:43, 3.81s/it] {'loss': 1.0927, 'grad_norm': 0.0005846396035320099, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:02<10:43, 3.81s/it] 68%|██████▊ | 352/520 [22:06<10:37, 3.79s/it] {'loss': 1.2055, 'grad_norm': 0.0005862380468716775, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:06<10:37, 3.79s/it] 68%|██████▊ | 353/520 [22:09<10:26, 3.75s/it] {'loss': 1.1284, 'grad_norm': 0.0005256951441983329, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:09<10:26, 3.75s/it] 68%|██████▊ | 354/520 [22:13<10:14, 3.70s/it] {'loss': 1.2217, 'grad_norm': 0.0005750143167861925, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:13<10:14, 3.70s/it] 68%|██████▊ | 355/520 [22:17<10:06, 3.67s/it] {'loss': 1.1534, 'grad_norm': 0.0006031202817547566, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:17<10:06, 3.67s/it] 68%|██████▊ | 356/520 [22:20<10:00, 3.66s/it] {'loss': 1.1557, 'grad_norm': 0.0006350602870796973, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:20<10:00, 3.66s/it] 69%|██████▊ | 357/520 [22:24<09:56, 3.66s/it] {'loss': 1.1879, 'grad_norm': 0.0005846291841610786, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:24<09:56, 3.66s/it] 69%|██████▉ | 358/520 [22:28<09:52, 3.66s/it] {'loss': 1.1168, 'grad_norm': 0.0006160051170212267, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:28<09:52, 3.66s/it] 69%|██████▉ | 359/520 [22:31<09:48, 3.65s/it] {'loss': 1.1641, 'grad_norm': 0.0006425981861985361, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:31<09:48, 3.65s/it] 69%|██████▉ | 360/520 [22:35<09:44, 3.65s/it] {'loss': 1.1712, 'grad_norm': 0.0006493333913505678, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:35<09:44, 3.65s/it] 69%|██████▉ | 361/520 [22:39<09:41, 3.65s/it] {'loss': 1.1882, 'grad_norm': 0.000558988701687034, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:39<09:41, 3.65s/it] 70%|██████▉ | 362/520 [22:42<09:36, 3.65s/it] {'loss': 1.1651, 'grad_norm': 0.0006640134600001595, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:42<09:36, 3.65s/it] 70%|██████▉ | 363/520 [22:46<09:32, 3.65s/it] {'loss': 1.1923, 'grad_norm': 0.0006105364007716456, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:46<09:32, 3.65s/it] 70%|███████ | 364/520 [22:49<09:29, 3.65s/it] {'loss': 1.2015, 'grad_norm': 0.0006134648139814982, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:49<09:29, 3.65s/it] 70%|███████ | 365/520 [22:53<09:25, 3.65s/it] {'loss': 1.2455, 'grad_norm': 0.0006304583548044592, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:53<09:25, 3.65s/it] 70%|███████ | 366/520 [22:57<09:20, 3.64s/it] {'loss': 1.209, 'grad_norm': 0.0005909156555958067, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:57<09:20, 3.64s/it] 71%|███████ | 367/520 [23:00<09:17, 3.64s/it] {'loss': 1.2081, 'grad_norm': 0.0006240166224729531, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:00<09:17, 3.64s/it] 71%|███████ | 368/520 [23:04<09:14, 3.65s/it] {'loss': 1.0639, 'grad_norm': 0.0006249406187511212, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:04<09:14, 3.65s/it] 71%|███████ | 369/520 [23:08<09:10, 3.65s/it] {'loss': 1.1634, 'grad_norm': 0.0005562914982813252, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:08<09:10, 3.65s/it] 71%|███████ | 370/520 [23:11<09:06, 3.64s/it] {'loss': 1.123, 'grad_norm': 0.0005823871589799528, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:11<09:06, 3.64s/it] 71%|███████▏ | 371/520 [23:15<09:02, 3.64s/it] {'loss': 1.1181, 'grad_norm': 0.0006610161858321728, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:15<09:02, 3.64s/it] 72%|███████▏ | 372/520 [23:19<09:02, 3.66s/it] {'loss': 1.23, 'grad_norm': 0.0006406474177250783, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:19<09:02, 3.66s/it] 72%|███████▏ | 373/520 [23:22<08:57, 3.65s/it] {'loss': 1.1203, 'grad_norm': 0.0006410240106488209, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:22<08:57, 3.65s/it] 72%|███████▏ | 374/520 [23:26<08:52, 3.64s/it] {'loss': 1.21, 'grad_norm': 0.0006327828794177175, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:26<08:52, 3.64s/it] 72%|███████▏ | 375/520 [23:30<08:48, 3.64s/it] {'loss': 1.1264, 'grad_norm': 0.0006266684394533165, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:30<08:48, 3.64s/it] 72%|███████▏ | 376/520 [23:33<08:43, 3.64s/it] {'loss': 1.233, 'grad_norm': 0.0006090769841386878, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:33<08:43, 3.64s/it] 72%|███████▎ | 377/520 [23:37<08:39, 3.63s/it] {'loss': 1.1631, 'grad_norm': 0.0006376214628805615, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:37<08:39, 3.63s/it] 73%|███████▎ | 378/520 [23:40<08:35, 3.63s/it] {'loss': 1.2269, 'grad_norm': 0.0005858868301395687, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:40<08:35, 3.63s/it] 73%|███████▎ | 379/520 [23:44<08:31, 3.63s/it] {'loss': 1.1943, 'grad_norm': 0.0005896494660030601, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:44<08:31, 3.63s/it] 73%|███████▎ | 380/520 [23:48<08:29, 3.64s/it] {'loss': 1.2078, 'grad_norm': 0.0006037612515406963, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:48<08:29, 3.64s/it] 73%|███████▎ | 381/520 [23:51<08:25, 3.64s/it] {'loss': 1.2047, 'grad_norm': 0.0005851008682379624, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:51<08:25, 3.64s/it] 73%|███████▎ | 382/520 [23:55<08:22, 3.64s/it] {'loss': 1.1796, 'grad_norm': 0.0005780510583140706, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:55<08:22, 3.64s/it] 74%|███████▎ | 383/520 [23:59<08:18, 3.64s/it] {'loss': 1.0456, 'grad_norm': 0.0007023202028252508, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:59<08:18, 3.64s/it] 74%|███████▍ | 384/520 [24:02<08:13, 3.63s/it] {'loss': 1.203, 'grad_norm': 0.0005600066006131485, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:02<08:13, 3.63s/it] 74%|███████▍ | 385/520 [24:06<08:09, 3.63s/it] {'loss': 1.1858, 'grad_norm': 0.0005708393581356607, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:06<08:09, 3.63s/it] 74%|███████▍ | 386/520 [24:10<08:06, 3.63s/it] {'loss': 1.1401, 'grad_norm': 0.0005525885488132204, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:10<08:06, 3.63s/it] 74%|███████▍ | 387/520 [24:13<08:03, 3.63s/it] {'loss': 1.2295, 'grad_norm': 0.0006034992590502965, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:13<08:03, 3.63s/it] 75%|███████▍ | 388/520 [24:17<07:59, 3.63s/it] {'loss': 1.0988, 'grad_norm': 0.0005903050073598621, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:17<07:59, 3.63s/it] 75%|███████▍ | 389/520 [24:20<07:55, 3.63s/it] {'loss': 1.1443, 'grad_norm': 0.0007614265825015862, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:20<07:55, 3.63s/it] 75%|███████▌ | 390/520 [24:24<07:52, 3.63s/it] {'loss': 1.2113, 'grad_norm': 0.0006182587969816486, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:24<07:52, 3.63s/it] 75%|███████▌ | 391/520 [24:28<07:49, 3.64s/it] {'loss': 1.2727, 'grad_norm': 0.0006351195604231121, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:28<07:49, 3.64s/it] 75%|███████▌ | 392/520 [24:31<07:46, 3.65s/it] {'loss': 1.0997, 'grad_norm': 0.000618827227369226, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:31<07:46, 3.65s/it] 76%|███████▌ | 393/520 [24:35<07:43, 3.65s/it] {'loss': 1.0896, 'grad_norm': 0.0005083017799965602, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:35<07:43, 3.65s/it] 76%|███████▌ | 394/520 [24:39<07:39, 3.65s/it] {'loss': 1.1692, 'grad_norm': 0.0006533066085213731, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:39<07:39, 3.65s/it] 76%|███████▌ | 395/520 [24:42<07:35, 3.64s/it] {'loss': 1.1328, 'grad_norm': 0.000668464918944676, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:42<07:35, 3.64s/it] 76%|███████▌ | 396/520 [24:46<07:29, 3.63s/it] {'loss': 1.2114, 'grad_norm': 0.0006592277866224397, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:46<07:29, 3.63s/it] 76%|███████▋ | 397/520 [24:50<07:25, 3.62s/it] {'loss': 1.1864, 'grad_norm': 0.0005884667567500175, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:50<07:25, 3.62s/it] 77%|███████▋ | 398/520 [24:53<07:22, 3.63s/it] {'loss': 1.1839, 'grad_norm': 0.0006429929464652327, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:53<07:22, 3.63s/it] 77%|███████▋ | 399/520 [24:57<07:18, 3.63s/it] {'loss': 1.1217, 'grad_norm': 0.0005783243337732265, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:57<07:18, 3.63s/it] 77%|███████▋ | 400/520 [25:00<07:15, 3.63s/it] {'loss': 1.1541, 'grad_norm': 0.0005615225570103739, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:00<07:15, 3.63s/it] 77%|███████▋ | 401/520 [25:04<07:10, 3.62s/it] {'loss': 1.0241, 'grad_norm': 0.0006853217369099287, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:04<07:10, 3.62s/it] 77%|███████▋ | 402/520 [25:08<07:07, 3.62s/it] {'loss': 1.1519, 'grad_norm': 0.0006333073090730326, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:08<07:07, 3.62s/it] 78%|███████▊ | 403/520 [25:11<07:02, 3.61s/it] {'loss': 1.1724, 'grad_norm': 0.0006611701996981851, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:11<07:02, 3.61s/it] 78%|███████▊ | 404/520 [25:15<06:58, 3.61s/it] {'loss': 1.0845, 'grad_norm': 0.0007008364176297309, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:15<06:58, 3.61s/it] 78%|███████▊ | 405/520 [25:18<06:55, 3.62s/it] {'loss': 1.1368, 'grad_norm': 0.0006146881462479483, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:18<06:55, 3.62s/it] 78%|███████▊ | 406/520 [25:22<06:51, 3.61s/it] {'loss': 1.0555, 'grad_norm': 0.0007623607465967548, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:22<06:51, 3.61s/it] 78%|███████▊ | 407/520 [25:26<06:47, 3.61s/it] {'loss': 1.2494, 'grad_norm': 0.0006653455045095614, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:26<06:47, 3.61s/it] 78%|███████▊ | 408/520 [25:29<06:43, 3.60s/it] {'loss': 1.1664, 'grad_norm': 0.0007770163531025693, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:29<06:43, 3.60s/it] 79%|███████▊ | 409/520 [25:33<06:39, 3.60s/it] {'loss': 1.2788, 'grad_norm': 0.0006993587061333537, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:33<06:39, 3.60s/it] 79%|███████▉ | 410/520 [25:37<06:37, 3.62s/it] {'loss': 1.0244, 'grad_norm': 0.0006234311482047578, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:37<06:37, 3.62s/it] 79%|███████▉ | 411/520 [25:40<06:39, 3.66s/it] {'loss': 1.2605, 'grad_norm': 0.0006562836188638751, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:40<06:39, 3.66s/it] 79%|███████▉ | 412/520 [25:44<06:38, 3.69s/it] {'loss': 1.1709, 'grad_norm': 0.0006062965167824372, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:44<06:38, 3.69s/it] 79%|███████▉ | 413/520 [25:48<06:33, 3.68s/it] {'loss': 1.1493, 'grad_norm': 0.00059418674352004, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:48<06:33, 3.68s/it] 80%|███████▉ | 414/520 [25:51<06:27, 3.66s/it] {'loss': 0.9634, 'grad_norm': 0.0005099208580145799, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:51<06:27, 3.66s/it] 80%|███████▉ | 415/520 [25:55<06:23, 3.65s/it] {'loss': 1.1542, 'grad_norm': 0.000584126581285084, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:55<06:23, 3.65s/it] 80%|████████ | 416/520 [25:59<06:18, 3.64s/it] {'loss': 1.0582, 'grad_norm': 0.0007401721586192531, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:59<06:18, 3.64s/it] 80%|████████ | 417/520 [26:02<06:14, 3.64s/it] {'loss': 1.2222, 'grad_norm': 0.0006190176964848145, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:02<06:14, 3.64s/it] 80%|████████ | 418/520 [26:06<06:09, 3.63s/it] {'loss': 1.215, 'grad_norm': 0.0006128690180753783, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:06<06:09, 3.63s/it] 81%|████████ | 419/520 [26:09<06:06, 3.63s/it] {'loss': 1.2081, 'grad_norm': 0.000666401658769903, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:09<06:06, 3.63s/it] 81%|████████ | 420/520 [26:13<06:02, 3.62s/it] {'loss': 1.101, 'grad_norm': 0.0006473028052858196, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:13<06:02, 3.62s/it] 81%|████████ | 421/520 [26:17<05:58, 3.62s/it] {'loss': 1.0381, 'grad_norm': 0.0006783366863354466, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:17<05:58, 3.62s/it] 81%|████████ | 422/520 [26:20<05:54, 3.62s/it] {'loss': 1.1584, 'grad_norm': 0.0006418944929197953, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:20<05:54, 3.62s/it] 81%|████████▏ | 423/520 [26:24<05:53, 3.64s/it] {'loss': 1.1266, 'grad_norm': 0.0006592804712879954, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:24<05:53, 3.64s/it] 82%|████████▏ | 424/520 [26:28<05:51, 3.66s/it] {'loss': 1.2341, 'grad_norm': 0.0006030158064739634, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:28<05:51, 3.66s/it] 82%|████████▏ | 425/520 [26:31<05:52, 3.71s/it] {'loss': 1.1468, 'grad_norm': 0.0006024324184982462, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:31<05:52, 3.71s/it] 82%|████████▏ | 426/520 [26:35<05:50, 3.73s/it] {'loss': 1.1752, 'grad_norm': 0.000836443797344411, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:35<05:50, 3.73s/it] 82%|████████▏ | 427/520 [26:39<05:48, 3.75s/it] {'loss': 1.0794, 'grad_norm': 0.0005929343914459626, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:39<05:48, 3.75s/it] 82%|████████▏ | 428/520 [26:43<05:46, 3.76s/it] {'loss': 1.0688, 'grad_norm': 0.0006577459926114377, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:43<05:46, 3.76s/it] 82%|████████▎ | 429/520 [26:47<05:43, 3.78s/it] {'loss': 1.1644, 'grad_norm': 0.0006589620232489938, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:47<05:43, 3.78s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:50<05:41, 3.79s/it] {'loss': 1.1655, 'grad_norm': 0.0005829457093800095, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:50<05:41, 3.79s/it] 83%|████████▎ | 431/520 [26:54<05:37, 3.80s/it] {'loss': 1.1236, 'grad_norm': 0.0006361819716925313, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:54<05:37, 3.80s/it] 83%|████████▎ | 432/520 [26:58<05:34, 3.81s/it] {'loss': 1.0742, 'grad_norm': 0.0006328375500299523, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:58<05:34, 3.81s/it] 83%|████████▎ | 433/520 [27:02<05:30, 3.80s/it] {'loss': 1.2059, 'grad_norm': 0.0006391801539411159, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:02<05:30, 3.80s/it] 83%|████████▎ | 434/520 [27:06<05:27, 3.81s/it] {'loss': 0.9586, 'grad_norm': 0.0006207388080226827, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:06<05:27, 3.81s/it] 84%|████████▎ | 435/520 [27:10<05:23, 3.81s/it] {'loss': 1.2357, 'grad_norm': 0.0006752569971712447, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:10<05:23, 3.81s/it] 84%|████████▍ | 436/520 [27:13<05:18, 3.79s/it] {'loss': 1.0465, 'grad_norm': 0.0006416341645993495, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:13<05:18, 3.79s/it] 84%|████████▍ | 437/520 [27:17<05:10, 3.74s/it] {'loss': 1.2576, 'grad_norm': 0.0006246024462418573, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:17<05:10, 3.74s/it] 84%|████████▍ | 438/520 [27:21<05:03, 3.71s/it] {'loss': 1.0839, 'grad_norm': 0.0006297209880265319, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:21<05:03, 3.71s/it] 84%|████████▍ | 439/520 [27:24<04:58, 3.69s/it] {'loss': 1.107, 'grad_norm': 0.0004956784062257911, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:24<04:58, 3.69s/it] 85%|████████▍ | 440/520 [27:28<04:53, 3.67s/it] {'loss': 1.1156, 'grad_norm': 0.0006690268470793458, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:28<04:53, 3.67s/it] 85%|████████▍ | 441/520 [27:31<04:50, 3.68s/it] {'loss': 1.1198, 'grad_norm': 0.000703057180859117, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:32<04:50, 3.68s/it] 85%|████████▌ | 442/520 [27:35<04:45, 3.66s/it] {'loss': 1.1797, 'grad_norm': 0.0006777284833157633, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:35<04:45, 3.66s/it] 85%|████████▌ | 443/520 [27:39<04:40, 3.65s/it] {'loss': 1.1889, 'grad_norm': 0.0006014375040622548, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:39<04:40, 3.65s/it] 85%|████████▌ | 444/520 [27:42<04:36, 3.64s/it] 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{'loss': 1.268, 'grad_norm': 0.0005684831145800162, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:36<00:49, 3.82s/it] 98%|█████████▊| 508/520 [31:40<00:45, 3.80s/it] {'loss': 1.2483, 'grad_norm': 0.0006315364676364845, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:40<00:45, 3.80s/it] 98%|█████████▊| 509/520 [31:44<00:41, 3.78s/it] {'loss': 1.2206, 'grad_norm': 0.0006144048484908378, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:44<00:41, 3.78s/it] 98%|█████████▊| 510/520 [31:47<00:37, 3.78s/it] {'loss': 1.1685, 'grad_norm': 0.0006122963419321813, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:47<00:37, 3.78s/it] 98%|█████████▊| 511/520 [31:51<00:33, 3.77s/it] {'loss': 1.1353, 'grad_norm': 0.0005943876541247802, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:51<00:33, 3.77s/it] 98%|█████████▊| 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[32:10<00:15, 3.76s/it] 99%|█████████▉| 517/520 [32:14<00:11, 3.72s/it] {'loss': 1.1668, 'grad_norm': 0.0005853887290990237, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:14<00:11, 3.72s/it] 100%|█████████▉| 518/520 [32:17<00:07, 3.68s/it] {'loss': 1.1604, 'grad_norm': 0.0006372619431068184, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:17<00:07, 3.68s/it] 100%|█████████▉| 519/520 [32:21<00:03, 3.65s/it] {'loss': 1.1405, 'grad_norm': 0.0005963872630794835, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:21<00:03, 3.65s/it] 100%|██████████| 520/520 [32:25<00:00, 3.89s/it] {'loss': 1.1285, 'grad_norm': 0.0005663043846728258, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:25<00:00, 3.89s/it] {'train_runtime': 1945.733, 'train_samples_per_second': 34.192, 'train_steps_per_second': 0.267, 'train_loss': 1.216097347323711, 'epoch': 1.0} + 100%|██████████| 520/520 [32:25<00:00, 3.89s/it] 100%|██████████| 520/520 [32:25<00:00, 3.74s/it] +[2025-10-17 21:58:26,535] [INFO] [launch.py:348:main] Process 679779 exits successfully. +[2025-10-17 21:58:26,535] [INFO] [launch.py:348:main] Process 679778 exits successfully. +[2025-10-17 21:58:27,537] [INFO] [launch.py:348:main] Process 679783 exits successfully. +[2025-10-17 21:58:27,538] [INFO] [launch.py:348:main] Process 679780 exits successfully. +[2025-10-17 21:58:27,538] [INFO] [launch.py:348:main] Process 679782 exits successfully. +[2025-10-17 21:58:27,539] [INFO] [launch.py:348:main] Process 679784 exits successfully. +[2025-10-17 21:58:27,539] [INFO] [launch.py:348:main] Process 679781 exits successfully. +[2025-10-17 21:58:31,544] [INFO] [launch.py:348:main] Process 679777 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_1.9_2e-1_connector-7.0_1.9_2e-1_ablation_20251017_212430.log +Timestamp: 2025-10-17 21:58:34 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation_20251017_215834.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation_20251017_215834.log new file mode 100644 index 0000000000000000000000000000000000000000..356984efb10ffba287fbb9098cdd305cad825a03 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation_20251017_215834.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation_20251017_215834.log +Timestamp: 2025-10-17 21:58:34 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:58:36,813] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:39,721] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 21:58:39,722] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 2.1 --temperature_mlp_text 2.1 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 2.1 --temperature_mlp_vision 2.1 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 2.1 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:58:42,269] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:43,312] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 21:58:43,312] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 21:58:43,312] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 21:58:43,312] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 21:58:43,312] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 21:58:43,312] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 21:58:43,312] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 21:58:43,315] [INFO] [launch.py:253:main] process 701571 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,316] [INFO] [launch.py:253:main] process 701572 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,318] [INFO] [launch.py:253:main] process 701573 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,320] [INFO] [launch.py:253:main] process 701574 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,322] [INFO] [launch.py:253:main] process 701575 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,324] [INFO] [launch.py:253:main] process 701576 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,326] [INFO] [launch.py:253:main] process 701577 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 21:58:43,328] [INFO] [launch.py:253:main] process 701578 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 21:58:50,177] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,319] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,522] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,556] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,572] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,593] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:50,593] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 21:58:50,623] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,624] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,651] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 21:58:50,740] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:50,943] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:50,967] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:50,987] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:51,042] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:51,046] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 21:58:51,068] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.1, 'temperature_mlp': 2.1, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.1, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.1, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.1, + "temperature_mlp": 2.1, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:701571:701571 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701576:701576 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701577:701577 [6] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701573:701573 [2] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701575:701575 [4] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:701572:701572 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:701574:701574 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:701574:701574 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:701574:701574 [3] 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[7] NCCL INFO NVLS multicast support is not available on dev 7 +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Setting affinity for GPU 2 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO NVLS multicast support is not available on dev 2 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO NVLS multicast support is not available on dev 0 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Setting affinity for GPU 3 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO NVLS multicast support is not available on dev 3 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Setting affinity for GPU 6 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Setting affinity for GPU 5 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO NVLS multicast support is not available on dev 6 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO NVLS multicast support is not available on dev 5 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO comm 0x55db183b1f00 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO comm 0x5636448392e0 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO comm 0x556448ba5090 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO comm 0x5571d34f21a0 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO comm 0x5607fd62f6b0 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO comm 0x55df2f1983d0 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 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6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701578:703182 [7] NCCL INFO ncclCommInitRank comm 0x5571d34f21a0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701574:703181 [3] NCCL INFO ncclCommInitRank comm 0x5607fd62f6b0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701571:703175 [0] NCCL INFO ncclCommInitRank comm 0x559a80833270 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701573:703178 [2] NCCL INFO ncclCommInitRank comm 0x55df2f1983d0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701575:703179 [4] NCCL INFO ncclCommInitRank comm 0x556448ba5090 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701577:703177 [6] NCCL INFO ncclCommInitRank comm 0x55db183b1f00 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701572:703180 [1] NCCL INFO ncclCommInitRank comm 0x55683e893c40 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x35d3bdfe72189518 - Init COMPLETE +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:701576:703176 [5] NCCL INFO ncclCommInitRank comm 0x5636448392e0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x35d3bdfe72189518 - Init COMPLETE +[2025-10-17 21:59:37,041] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 21:59:38,852] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 21:59:56,854 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 21:59:56,860 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters 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+language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters 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4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters 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+language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:006->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO comm 0x7fcab806afb0 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701571:708089 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701575:708095 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701573:708096 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701574:708093 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701572:708094 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701577:708090 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701576:708091 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:701578:708092 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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'grad_norm': 0.010001668437028445, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:28<48:21, 5.62s/it] 1%| | 5/520 [00:32<42:54, 5.00s/it] {'loss': 1.789, 'grad_norm': 0.0054742527010693005, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:32<42:54, 5.00s/it] 1%| | 6/520 [00:36<39:39, 4.63s/it] {'loss': 1.4393, 'grad_norm': 0.0018344504079837794, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:36<39:39, 4.63s/it] 1%|▏ | 7/520 [00:40<37:25, 4.38s/it] {'loss': 1.5007, 'grad_norm': 0.0018463115258857924, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:40<37:25, 4.38s/it] 2%|▏ | 8/520 [00:44<37:37, 4.41s/it] {'loss': 1.5405, 'grad_norm': 0.0018554896544101921, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:44<37:37, 4.41s/it] 2%|▏ | 9/520 [00:48<36:04, 4.24s/it] {'loss': 1.6002, 'grad_norm': 0.001632332329987393, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:48<36:04, 4.24s/it] 2%|▏ | 10/520 [00:52<34:58, 4.11s/it] {'loss': 1.4125, 'grad_norm': 0.0013174335766068938, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:52<34:58, 4.11s/it] 2%|▏ | 11/520 [00:56<34:24, 4.06s/it] {'loss': 1.4604, 'grad_norm': 0.0013241379505436392, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:56<34:24, 4.06s/it] 2%|▏ | 12/520 [01:00<33:49, 4.00s/it] {'loss': 1.3525, 'grad_norm': 0.0009512292421719958, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:00<33:49, 4.00s/it][2025-10-17 22:01:05,850] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:04<35:09, 4.16s/it] {'loss': 1.4028, 'grad_norm': 0.0009382713408581046, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:04<35:09, 4.16s/it] 3%|▎ | 14/520 [01:08<34:18, 4.07s/it] {'loss': 1.4519, 'grad_norm': 0.0011871768965122692, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:08<34:18, 4.07s/it] 3%|▎ | 15/520 [01:12<33:41, 4.00s/it] {'loss': 1.3902, 'grad_norm': 0.0009824779512558064, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:12<33:41, 4.00s/it] 3%|▎ | 16/520 [01:16<33:17, 3.96s/it] {'loss': 1.3507, 'grad_norm': 0.0010008025538115852, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:16<33:17, 3.96s/it] 3%|▎ | 17/520 [01:20<33:02, 3.94s/it] {'loss': 1.4642, 'grad_norm': 0.0008979705954078141, 'learning_rate': 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0.0008201957291432617, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:12<13:19, 3.77s/it] 59%|█████▉ | 309/520 [19:15<13:09, 3.74s/it] {'loss': 1.1657, 'grad_norm': 0.0006992764192351254, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:15<13:09, 3.74s/it] 60%|█████▉ | 310/520 [19:19<13:00, 3.72s/it] {'loss': 1.1467, 'grad_norm': 0.0006833669845140286, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:19<13:00, 3.72s/it] 60%|█████▉ | 311/520 [19:23<12:52, 3.70s/it] {'loss': 1.1253, 'grad_norm': 0.0007126552917282693, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:23<12:52, 3.70s/it] 60%|██████ | 312/520 [19:26<12:45, 3.68s/it] {'loss': 1.1151, 'grad_norm': 0.0006920562778049311, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:26<12:45, 3.68s/it] 60%|██████ | 313/520 [19:30<12:39, 3.67s/it] {'loss': 1.1, 'grad_norm': 0.0006387728597848012, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:30<12:39, 3.67s/it] 60%|██████ | 314/520 [19:34<12:56, 3.77s/it] {'loss': 1.138, 'grad_norm': 0.0006702389322930725, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:34<12:56, 3.77s/it] 61%|██████ | 315/520 [19:38<12:43, 3.72s/it] {'loss': 1.1803, 'grad_norm': 0.000875425526866268, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:38<12:43, 3.72s/it] 61%|██████ | 316/520 [19:42<13:00, 3.83s/it] {'loss': 1.1235, 'grad_norm': 0.0007425390216382153, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:42<13:00, 3.83s/it] 61%|██████ | 317/520 [19:45<12:45, 3.77s/it] {'loss': 1.1265, 'grad_norm': 0.0006020494233714052, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:45<12:45, 3.77s/it] 61%|██████ | 318/520 [19:49<12:35, 3.74s/it] {'loss': 1.2373, 'grad_norm': 0.000730346105099712, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:49<12:35, 3.74s/it] 61%|██████▏ | 319/520 [19:53<12:47, 3.82s/it] {'loss': 1.1183, 'grad_norm': 0.0006058132392676421, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:53<12:47, 3.82s/it] 62%|██████▏ | 320/520 [19:57<12:33, 3.77s/it] {'loss': 1.0641, 'grad_norm': 0.0007050463001498363, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:57<12:33, 3.77s/it] 62%|██████▏ | 321/520 [20:00<12:23, 3.74s/it] {'loss': 1.2581, 'grad_norm': 0.0007158945573726087, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:00<12:23, 3.74s/it] 62%|██████▏ | 322/520 [20:04<12:13, 3.70s/it] {'loss': 1.0838, 'grad_norm': 0.0006581173868596907, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:04<12:13, 3.70s/it] 62%|██████▏ | 323/520 [20:07<12:04, 3.68s/it] {'loss': 1.1529, 'grad_norm': 0.0006661059723459095, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:07<12:04, 3.68s/it] 62%|██████▏ | 324/520 [20:11<11:59, 3.67s/it] {'loss': 1.2042, 'grad_norm': 0.0006902330287384393, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:11<11:59, 3.67s/it] 62%|██████▎ | 325/520 [20:15<11:53, 3.66s/it] {'loss': 1.1998, 'grad_norm': 0.0007280013838325154, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:15<11:53, 3.66s/it] 63%|██████▎ | 326/520 [20:18<11:48, 3.65s/it] {'loss': 1.1989, 'grad_norm': 0.0007069344868804168, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:18<11:48, 3.65s/it] 63%|██████▎ | 327/520 [20:22<11:43, 3.64s/it] {'loss': 1.1823, 'grad_norm': 0.0006782108781406597, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:22<11:43, 3.64s/it] 63%|██████▎ | 328/520 [20:26<11:38, 3.64s/it] {'loss': 1.2386, 'grad_norm': 0.0007373636387263627, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:26<11:38, 3.64s/it] 63%|██████▎ | 329/520 [20:29<11:34, 3.64s/it] {'loss': 1.1234, 'grad_norm': 0.0006039996466641968, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:29<11:34, 3.64s/it] 63%|██████▎ | 330/520 [20:33<11:30, 3.63s/it] {'loss': 1.1958, 'grad_norm': 0.0006457379332781423, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:33<11:30, 3.63s/it] 64%|██████▎ | 331/520 [20:37<11:29, 3.65s/it] {'loss': 1.1588, 'grad_norm': 0.0006933194425428943, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:37<11:29, 3.65s/it] 64%|██████▍ | 332/520 [20:40<11:24, 3.64s/it] {'loss': 1.2121, 'grad_norm': 0.0006116411402814359, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:40<11:24, 3.64s/it] 64%|██████▍ | 333/520 [20:44<11:21, 3.65s/it] {'loss': 1.2902, 'grad_norm': 0.0007373755615958936, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:44<11:21, 3.65s/it] 64%|██████▍ | 334/520 [20:48<11:20, 3.66s/it] {'loss': 1.2011, 'grad_norm': 0.0007372372295141236, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:48<11:20, 3.66s/it] 64%|██████▍ | 335/520 [20:51<11:17, 3.66s/it] {'loss': 1.2021, 'grad_norm': 0.0006580977379058621, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:51<11:17, 3.66s/it] 65%|██████▍ | 336/520 [20:55<11:12, 3.65s/it] {'loss': 1.1105, 'grad_norm': 0.0007360593193756122, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:55<11:12, 3.65s/it] 65%|██████▍ | 337/520 [20:58<11:08, 3.65s/it] {'loss': 1.0999, 'grad_norm': 0.000665986612714414, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:58<11:08, 3.65s/it] 65%|██████▌ | 338/520 [21:02<11:03, 3.65s/it] {'loss': 1.2045, 'grad_norm': 0.000659673137926611, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:02<11:03, 3.65s/it] 65%|██████▌ | 339/520 [21:06<11:01, 3.65s/it] {'loss': 1.1522, 'grad_norm': 0.0006926013178036532, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:06<11:01, 3.65s/it] 65%|██████▌ | 340/520 [21:09<10:57, 3.65s/it] {'loss': 1.1411, 'grad_norm': 0.0006663625020057263, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:09<10:57, 3.65s/it] 66%|██████▌ | 341/520 [21:13<10:52, 3.64s/it] {'loss': 1.168, 'grad_norm': 0.0007182766348791937, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:13<10:52, 3.64s/it] 66%|██████▌ | 342/520 [21:17<10:47, 3.64s/it] {'loss': 1.188, 'grad_norm': 0.0008142338255381215, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:17<10:47, 3.64s/it] 66%|██████▌ | 343/520 [21:20<10:44, 3.64s/it] {'loss': 1.1387, 'grad_norm': 0.0006880680022168979, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:20<10:44, 3.64s/it] 66%|██████▌ | 344/520 [21:24<10:40, 3.64s/it] {'loss': 1.1256, 'grad_norm': 0.0006535779432221215, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:24<10:40, 3.64s/it] 66%|██████▋ | 345/520 [21:28<10:35, 3.63s/it] {'loss': 1.226, 'grad_norm': 0.0006961169699569054, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:28<10:35, 3.63s/it] 67%|██████▋ | 346/520 [21:31<10:32, 3.64s/it] {'loss': 1.1617, 'grad_norm': 0.0007469867262527212, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:31<10:32, 3.64s/it] 67%|██████▋ | 347/520 [21:35<10:28, 3.63s/it] {'loss': 1.1424, 'grad_norm': 0.0006211965016676886, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:35<10:28, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:38<10:24, 3.63s/it] {'loss': 1.0991, 'grad_norm': 0.0009191510534204197, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:38<10:24, 3.63s/it] 67%|██████▋ | 349/520 [21:42<10:21, 3.63s/it] {'loss': 1.1357, 'grad_norm': 0.0006629960244190697, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:42<10:21, 3.63s/it] 67%|██████▋ | 350/520 [21:46<10:19, 3.65s/it] {'loss': 1.181, 'grad_norm': 0.0008161560601964106, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:46<10:19, 3.65s/it] 68%|██████▊ | 351/520 [21:49<10:17, 3.65s/it] {'loss': 1.0912, 'grad_norm': 0.0006452555095689791, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:49<10:17, 3.65s/it] 68%|██████▊ | 352/520 [21:53<10:12, 3.65s/it] {'loss': 1.2055, 'grad_norm': 0.0006440893773559487, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:53<10:12, 3.65s/it] 68%|██████▊ | 353/520 [21:57<10:11, 3.66s/it] {'loss': 1.1299, 'grad_norm': 0.0005977710883678612, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:57<10:11, 3.66s/it] 68%|██████▊ | 354/520 [22:00<10:07, 3.66s/it] {'loss': 1.2254, 'grad_norm': 0.0006331725193440306, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:00<10:07, 3.66s/it] 68%|██████▊ | 355/520 [22:04<10:02, 3.65s/it] {'loss': 1.1512, 'grad_norm': 0.0006556698770500946, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:04<10:02, 3.65s/it] 68%|██████▊ | 356/520 [22:08<09:59, 3.66s/it] {'loss': 1.1547, 'grad_norm': 0.0006966269376825463, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:08<09:59, 3.66s/it] 69%|██████▊ | 357/520 [22:11<09:55, 3.65s/it] {'loss': 1.1857, 'grad_norm': 0.0006342612242428525, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:11<09:55, 3.65s/it] 69%|██████▉ | 358/520 [22:15<09:52, 3.66s/it] {'loss': 1.1165, 'grad_norm': 0.00066650969690437, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:15<09:52, 3.66s/it] 69%|██████▉ | 359/520 [22:19<09:49, 3.66s/it] {'loss': 1.1644, 'grad_norm': 0.0007333557368579776, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:19<09:49, 3.66s/it] 69%|██████▉ | 360/520 [22:22<09:46, 3.67s/it] {'loss': 1.1718, 'grad_norm': 0.0007178353536693612, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:22<09:46, 3.67s/it] 69%|██████▉ | 361/520 [22:26<09:44, 3.68s/it] {'loss': 1.1883, 'grad_norm': 0.0006247544843023337, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:26<09:44, 3.68s/it] 70%|██████▉ | 362/520 [22:30<09:41, 3.68s/it] {'loss': 1.1642, 'grad_norm': 0.0007207348383413804, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:30<09:41, 3.68s/it] 70%|██████▉ | 363/520 [22:33<09:37, 3.68s/it] {'loss': 1.1901, 'grad_norm': 0.0006669810720673667, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:33<09:37, 3.68s/it] 70%|███████ | 364/520 [22:37<09:35, 3.69s/it] {'loss': 1.2037, 'grad_norm': 0.000683019500056799, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:37<09:35, 3.69s/it] 70%|███████ | 365/520 [22:41<09:33, 3.70s/it] {'loss': 1.2444, 'grad_norm': 0.0006983314422754978, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:41<09:33, 3.70s/it] 70%|███████ | 366/520 [22:45<09:30, 3.71s/it] {'loss': 1.2072, 'grad_norm': 0.0006365853036242793, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:45<09:30, 3.71s/it] 71%|███████ | 367/520 [22:48<09:27, 3.71s/it] {'loss': 1.2078, 'grad_norm': 0.0006808535861428651, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:48<09:27, 3.71s/it] 71%|███████ | 368/520 [22:52<09:22, 3.70s/it] {'loss': 1.0612, 'grad_norm': 0.0006929528633230082, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:52<09:22, 3.70s/it] 71%|███████ | 369/520 [22:56<09:17, 3.69s/it] {'loss': 1.1646, 'grad_norm': 0.0005928373244892923, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:56<09:17, 3.69s/it] 71%|███████ | 370/520 [22:59<09:12, 3.68s/it] {'loss': 1.1217, 'grad_norm': 0.0006345161122400185, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:59<09:12, 3.68s/it] 71%|███████▏ | 371/520 [23:03<09:07, 3.67s/it] {'loss': 1.1182, 'grad_norm': 0.0007127567108688639, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:03<09:07, 3.67s/it] 72%|███████▏ | 372/520 [23:07<09:02, 3.67s/it] {'loss': 1.2328, 'grad_norm': 0.000753656019495924, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:07<09:02, 3.67s/it] 72%|███████▏ | 373/520 [23:10<08:58, 3.66s/it] {'loss': 1.1214, 'grad_norm': 0.0006962281896384071, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:10<08:58, 3.66s/it] 72%|███████▏ | 374/520 [23:14<08:56, 3.67s/it] {'loss': 1.2076, 'grad_norm': 0.000679821337564992, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:14<08:56, 3.67s/it] 72%|███████▏ | 375/520 [23:18<08:52, 3.67s/it] {'loss': 1.127, 'grad_norm': 0.000688949277973335, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:18<08:52, 3.67s/it] 72%|███████▏ | 376/520 [23:21<08:51, 3.69s/it] {'loss': 1.2328, 'grad_norm': 0.0006496650679660404, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:21<08:51, 3.69s/it] 72%|███████▎ | 377/520 [23:25<08:46, 3.68s/it] {'loss': 1.1613, 'grad_norm': 0.0006875654360679643, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:25<08:46, 3.68s/it] 73%|███████▎ | 378/520 [23:29<08:40, 3.66s/it] {'loss': 1.2264, 'grad_norm': 0.0006457228808987219, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:29<08:40, 3.66s/it] 73%|███████▎ | 379/520 [23:32<08:34, 3.65s/it] {'loss': 1.1957, 'grad_norm': 0.0006395432244763891, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:32<08:34, 3.65s/it] 73%|███████▎ | 380/520 [23:36<08:30, 3.64s/it] {'loss': 1.2079, 'grad_norm': 0.0006601836565115082, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:36<08:30, 3.64s/it] 73%|███████▎ | 381/520 [23:40<08:26, 3.65s/it] {'loss': 1.2033, 'grad_norm': 0.0006358088732548947, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:40<08:26, 3.65s/it] 73%|███████▎ | 382/520 [23:43<08:27, 3.68s/it] {'loss': 1.18, 'grad_norm': 0.000637048642878779, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:43<08:27, 3.68s/it] 74%|███████▎ | 383/520 [23:47<08:21, 3.66s/it] {'loss': 1.0441, 'grad_norm': 0.0007671086117864601, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:47<08:21, 3.66s/it] 74%|███████▍ | 384/520 [23:51<08:16, 3.65s/it] {'loss': 1.2062, 'grad_norm': 0.0006181761814618503, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:51<08:16, 3.65s/it] 74%|███████▍ | 385/520 [23:54<08:12, 3.65s/it] {'loss': 1.1847, 'grad_norm': 0.0006238686637053918, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:54<08:12, 3.65s/it] 74%|███████▍ | 386/520 [23:58<08:07, 3.64s/it] {'loss': 1.1389, 'grad_norm': 0.000599104484967679, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:58<08:07, 3.64s/it] 74%|███████▍ | 387/520 [24:01<08:03, 3.64s/it] {'loss': 1.2307, 'grad_norm': 0.0006788533360933749, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:01<08:03, 3.64s/it] 75%|███████▍ | 388/520 [24:05<07:58, 3.63s/it] {'loss': 1.0978, 'grad_norm': 0.0006453709623737008, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:05<07:58, 3.63s/it] 75%|███████▍ | 389/520 [24:09<07:55, 3.63s/it] {'loss': 1.1426, 'grad_norm': 0.0007899259954300277, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:09<07:55, 3.63s/it] 75%|███████▌ | 390/520 [24:12<07:53, 3.64s/it] {'loss': 1.2099, 'grad_norm': 0.0006666901295503144, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:12<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:16<07:50, 3.65s/it] {'loss': 1.2724, 'grad_norm': 0.0006921460624237098, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:16<07:50, 3.65s/it] 75%|███████▌ | 392/520 [24:20<07:46, 3.65s/it] {'loss': 1.0975, 'grad_norm': 0.0006649102742099048, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:20<07:46, 3.65s/it] 76%|███████▌ | 393/520 [24:23<07:44, 3.66s/it] {'loss': 1.0897, 'grad_norm': 0.0005480132902917177, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:23<07:44, 3.66s/it] 76%|███████▌ | 394/520 [24:27<07:39, 3.65s/it] {'loss': 1.1679, 'grad_norm': 0.000699495963419287, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:27<07:39, 3.65s/it] 76%|███████▌ | 395/520 [24:31<07:35, 3.64s/it] {'loss': 1.1297, 'grad_norm': 0.0007310343234553706, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:31<07:35, 3.64s/it] 76%|███████▌ | 396/520 [24:34<07:31, 3.64s/it] {'loss': 1.2101, 'grad_norm': 0.0007124050690586928, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:34<07:31, 3.64s/it] 76%|███████▋ | 397/520 [24:38<07:28, 3.65s/it] {'loss': 1.184, 'grad_norm': 0.0006370518789412447, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:38<07:28, 3.65s/it] 77%|███████▋ | 398/520 [24:42<07:24, 3.64s/it] {'loss': 1.1833, 'grad_norm': 0.000703048487781189, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:42<07:24, 3.64s/it] 77%|███████▋ | 399/520 [24:45<07:22, 3.66s/it] {'loss': 1.1228, 'grad_norm': 0.0006266263611947936, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:45<07:22, 3.66s/it] 77%|███████▋ | 400/520 [24:49<07:20, 3.67s/it] {'loss': 1.1546, 'grad_norm': 0.0005996466814623386, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:49<07:20, 3.67s/it] 77%|███████▋ | 401/520 [24:53<07:16, 3.66s/it] {'loss': 1.0218, 'grad_norm': 0.0007353594959965294, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:53<07:16, 3.66s/it] 77%|███████▋ | 402/520 [24:56<07:11, 3.66s/it] {'loss': 1.1513, 'grad_norm': 0.0006999059025598625, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:56<07:11, 3.66s/it] 78%|███████▊ | 403/520 [25:00<07:07, 3.65s/it] {'loss': 1.171, 'grad_norm': 0.0007329614383436069, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:00<07:07, 3.65s/it] 78%|███████▊ | 404/520 [25:04<07:02, 3.64s/it] {'loss': 1.0826, 'grad_norm': 0.000766152604481198, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:04<07:02, 3.64s/it] 78%|███████▊ | 405/520 [25:07<07:00, 3.66s/it] {'loss': 1.1381, 'grad_norm': 0.0007013220776835362, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:07<07:00, 3.66s/it] 78%|███████▊ | 406/520 [25:11<07:00, 3.68s/it] {'loss': 1.0548, 'grad_norm': 0.000880395290111974, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:11<07:00, 3.68s/it] 78%|███████▊ | 407/520 [25:15<06:56, 3.69s/it] {'loss': 1.2477, 'grad_norm': 0.000735863241823544, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:15<06:56, 3.69s/it] 78%|███████▊ | 408/520 [25:18<06:52, 3.68s/it] {'loss': 1.1645, 'grad_norm': 0.0008588139071907529, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:18<06:52, 3.68s/it] 79%|███████▊ | 409/520 [25:22<06:48, 3.68s/it] {'loss': 1.2752, 'grad_norm': 0.0007564886771981608, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:22<06:48, 3.68s/it] 79%|███████▉ | 410/520 [25:26<06:42, 3.66s/it] {'loss': 1.0215, 'grad_norm': 0.0006723336233102182, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:26<06:42, 3.66s/it] 79%|███████▉ | 411/520 [25:29<06:37, 3.65s/it] {'loss': 1.2598, 'grad_norm': 0.000732586554004904, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:29<06:37, 3.65s/it] 79%|███████▉ | 412/520 [25:33<06:34, 3.65s/it] {'loss': 1.1695, 'grad_norm': 0.0006669929648833021, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:33<06:34, 3.65s/it] 79%|███████▉ | 413/520 [25:37<06:31, 3.66s/it] {'loss': 1.1494, 'grad_norm': 0.000632920446755547, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:37<06:31, 3.66s/it] 80%|███████▉ | 414/520 [25:40<06:28, 3.67s/it] {'loss': 0.9637, 'grad_norm': 0.000549186632306854, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:40<06:28, 3.67s/it] 80%|███████▉ | 415/520 [25:44<06:25, 3.67s/it] {'loss': 1.1524, 'grad_norm': 0.0006359546950208344, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:44<06:25, 3.67s/it] 80%|████████ | 416/520 [25:48<06:22, 3.67s/it] {'loss': 1.0573, 'grad_norm': 0.0007582562590033203, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:48<06:22, 3.67s/it] 80%|████████ | 417/520 [25:51<06:17, 3.67s/it] {'loss': 1.2199, 'grad_norm': 0.0006886596488492962, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:51<06:17, 3.67s/it] 80%|████████ | 418/520 [25:55<06:13, 3.66s/it] {'loss': 1.2128, 'grad_norm': 0.0006756620514055624, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:55<06:13, 3.66s/it] 81%|████████ | 419/520 [25:59<06:09, 3.66s/it] {'loss': 1.2064, 'grad_norm': 0.0007305556688684738, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:59<06:09, 3.66s/it] 81%|████████ | 420/520 [26:02<06:05, 3.66s/it] {'loss': 1.1002, 'grad_norm': 0.0007140054144775177, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:02<06:05, 3.66s/it] 81%|████████ | 421/520 [26:06<06:02, 3.66s/it] {'loss': 1.0364, 'grad_norm': 0.0007910474322538978, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:06<06:02, 3.66s/it] 81%|████████ | 422/520 [26:10<05:58, 3.66s/it] {'loss': 1.1564, 'grad_norm': 0.0007001468324544014, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:10<05:58, 3.66s/it] 81%|████████▏ | 423/520 [26:13<05:54, 3.65s/it] {'loss': 1.1249, 'grad_norm': 0.0007221515169413456, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:13<05:54, 3.65s/it] 82%|████████▏ | 424/520 [26:17<05:50, 3.65s/it] {'loss': 1.233, 'grad_norm': 0.0006381104693452506, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:17<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:20<05:45, 3.64s/it] {'loss': 1.1448, 'grad_norm': 0.0006415764312685851, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:20<05:45, 3.64s/it] 82%|████████▏ | 426/520 [26:24<05:40, 3.62s/it] {'loss': 1.1722, 'grad_norm': 0.0009242006446003192, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:24<05:40, 3.62s/it] 82%|████████▏ | 427/520 [26:28<05:36, 3.62s/it] {'loss': 1.0778, 'grad_norm': 0.0006463849901935264, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:28<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:31<05:31, 3.61s/it] {'loss': 1.0671, 'grad_norm': 0.0007184410753479659, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:31<05:31, 3.61s/it] 82%|████████▎ | 429/520 [26:35<05:29, 3.62s/it] {'loss': 1.161, 'grad_norm': 0.0007306009087143061, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:35<05:29, 3.62s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:39<05:26, 3.62s/it] {'loss': 1.1621, 'grad_norm': 0.0006289241258470559, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:39<05:26, 3.62s/it] 83%|████████▎ | 431/520 [26:42<05:22, 3.62s/it] {'loss': 1.1229, 'grad_norm': 0.0007112902062356856, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:42<05:22, 3.62s/it] 83%|████████▎ | 432/520 [26:46<05:18, 3.62s/it] {'loss': 1.0708, 'grad_norm': 0.0006774893771166022, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:46<05:18, 3.62s/it] 83%|████████▎ | 433/520 [26:49<05:14, 3.62s/it] {'loss': 1.2026, 'grad_norm': 0.0006995102188171504, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:49<05:14, 3.62s/it] 83%|████████▎ | 434/520 [26:53<05:13, 3.64s/it] {'loss': 0.9538, 'grad_norm': 0.0006674820907863217, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:53<05:13, 3.64s/it] 84%|████████▎ | 435/520 [26:57<05:09, 3.64s/it] {'loss': 1.2338, 'grad_norm': 0.0007306051685499519, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:57<05:09, 3.64s/it] 84%|████████▍ | 436/520 [27:00<05:06, 3.65s/it] {'loss': 1.0449, 'grad_norm': 0.0006951332105036947, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:00<05:06, 3.65s/it] 84%|████████▍ | 437/520 [27:04<05:01, 3.64s/it] {'loss': 1.2555, 'grad_norm': 0.0006784944437978445, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:04<05:01, 3.64s/it] 84%|████████▍ | 438/520 [27:08<04:57, 3.63s/it] {'loss': 1.081, 'grad_norm': 0.0006924518810979012, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:08<04:57, 3.63s/it] 84%|████████▍ | 439/520 [27:11<04:54, 3.63s/it] {'loss': 1.1066, 'grad_norm': 0.0005476047633499481, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:11<04:54, 3.63s/it] 85%|████████▍ | 440/520 [27:15<04:50, 3.63s/it] {'loss': 1.1111, 'grad_norm': 0.0006983072203659423, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:15<04:50, 3.63s/it] 85%|████████▍ | 441/520 [27:18<04:46, 3.62s/it] {'loss': 1.1197, 'grad_norm': 0.0007635806150753131, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:18<04:46, 3.62s/it] 85%|████████▌ | 442/520 [27:22<04:42, 3.62s/it] {'loss': 1.1762, 'grad_norm': 0.0007398451715764082, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:22<04:42, 3.62s/it] 85%|████████▌ | 443/520 [27:26<04:38, 3.62s/it] {'loss': 1.1866, 'grad_norm': 0.000649266362803232, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:26<04:38, 3.62s/it] 85%|████████▌ | 444/520 [27:29<04:35, 3.62s/it] {'loss': 1.1523, 'grad_norm': 0.0005934455046600947, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:29<04:35, 3.62s/it] 86%|████████▌ | 445/520 [27:33<04:31, 3.62s/it] {'loss': 1.0803, 'grad_norm': 0.0006551494018986631, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:33<04:31, 3.62s/it] 86%|████████▌ | 446/520 [27:37<04:27, 3.62s/it] {'loss': 1.1965, 'grad_norm': 0.0006043989817583363, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:37<04:27, 3.62s/it] 86%|████████▌ | 447/520 [27:40<04:25, 3.63s/it] {'loss': 1.1536, 'grad_norm': 0.0006518592864754847, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:40<04:25, 3.63s/it] 86%|████████▌ | 448/520 [27:44<04:20, 3.62s/it] {'loss': 1.1531, 'grad_norm': 0.0007198651256151205, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:44<04:20, 3.62s/it] 86%|████████▋ | 449/520 [27:47<04:17, 3.63s/it] {'loss': 1.1561, 'grad_norm': 0.000670245404702391, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:47<04:17, 3.63s/it] 87%|████████▋ | 450/520 [27:51<04:13, 3.62s/it] {'loss': 1.175, 'grad_norm': 0.0006663649462899198, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:51<04:13, 3.62s/it] 87%|████████▋ | 451/520 [27:55<04:09, 3.62s/it] {'loss': 1.178, 'grad_norm': 0.0006893473397001356, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:55<04:09, 3.62s/it] 87%|████████▋ | 452/520 [27:58<04:07, 3.63s/it] {'loss': 1.2, 'grad_norm': 0.0006332087991148968, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:58<04:07, 3.63s/it] 87%|████████▋ | 453/520 [28:02<04:03, 3.64s/it] {'loss': 1.1757, 'grad_norm': 0.0006468814628621414, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:02<04:03, 3.64s/it] 87%|████████▋ | 454/520 [28:06<03:59, 3.63s/it] {'loss': 1.0871, 'grad_norm': 0.0006802732814997368, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:06<03:59, 3.63s/it] 88%|████████▊ | 455/520 [28:09<03:55, 3.62s/it] {'loss': 1.2271, 'grad_norm': 0.0006657277614022267, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:09<03:55, 3.62s/it] 88%|████████▊ | 456/520 [28:13<03:51, 3.61s/it] {'loss': 1.1594, 'grad_norm': 0.0007213757230331859, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:13<03:51, 3.61s/it] 88%|████████▊ | 457/520 [28:16<03:47, 3.61s/it] {'loss': 1.0688, 'grad_norm': 0.0005714688072237397, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:16<03:47, 3.61s/it] 88%|████████▊ | 458/520 [28:20<03:44, 3.62s/it] {'loss': 1.2762, 'grad_norm': 0.0007487362734708655, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:20<03:44, 3.62s/it] 88%|████████▊ | 459/520 [28:24<03:40, 3.62s/it] {'loss': 1.2105, 'grad_norm': 0.0007364040756710551, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:24<03:40, 3.62s/it] 88%|████████▊ | 460/520 [28:27<03:37, 3.62s/it] {'loss': 1.1036, 'grad_norm': 0.0006499121966478429, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:27<03:37, 3.62s/it] 89%|████████▊ | 461/520 [28:31<03:34, 3.63s/it] {'loss': 1.1482, 'grad_norm': 0.000507243855919322, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:31<03:34, 3.63s/it] 89%|████████▉ | 462/520 [28:35<03:30, 3.63s/it] {'loss': 1.2438, 'grad_norm': 0.0006267993359405313, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:35<03:30, 3.63s/it] 89%|████████▉ | 463/520 [28:38<03:26, 3.63s/it] {'loss': 1.0705, 'grad_norm': 0.0007058361372081701, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:38<03:26, 3.63s/it] 89%|████████▉ | 464/520 [28:42<03:23, 3.63s/it] {'loss': 1.191, 'grad_norm': 0.0006969056170189111, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:42<03:23, 3.63s/it] 89%|████████▉ | 465/520 [28:45<03:19, 3.62s/it] {'loss': 1.2951, 'grad_norm': 0.0006917758771631086, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:45<03:19, 3.62s/it] 90%|████████▉ | 466/520 [28:49<03:15, 3.62s/it] {'loss': 1.1874, 'grad_norm': 0.0006771741514695258, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:49<03:15, 3.62s/it] 90%|████████▉ | 467/520 [28:53<03:12, 3.63s/it] {'loss': 1.1324, 'grad_norm': 0.0005997808243394614, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:53<03:12, 3.63s/it] 90%|█████████ | 468/520 [28:56<03:08, 3.62s/it] {'loss': 1.1554, 'grad_norm': 0.000752491996259287, 'learning_rate': 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{'loss': 1.268, 'grad_norm': 0.0006264905470793095, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:22<00:49, 3.79s/it] 98%|█████████▊| 508/520 [31:25<00:45, 3.78s/it] {'loss': 1.2457, 'grad_norm': 0.0006904004773772232, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:25<00:45, 3.78s/it] 98%|█████████▊| 509/520 [31:29<00:41, 3.78s/it] {'loss': 1.2184, 'grad_norm': 0.0006833247439308987, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:29<00:41, 3.78s/it] 98%|█████████▊| 510/520 [31:33<00:37, 3.79s/it] {'loss': 1.1669, 'grad_norm': 0.0006611414269453871, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:33<00:37, 3.79s/it] 98%|█████████▊| 511/520 [31:37<00:34, 3.79s/it] {'loss': 1.1336, 'grad_norm': 0.000643919881758941, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:37<00:34, 3.79s/it] 98%|█████████▊| 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[31:55<00:14, 3.69s/it] 99%|█████████▉| 517/520 [31:59<00:10, 3.65s/it] {'loss': 1.1661, 'grad_norm': 0.0006449974698106574, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [31:59<00:10, 3.65s/it] 100%|█████████▉| 518/520 [32:02<00:07, 3.62s/it] {'loss': 1.1572, 'grad_norm': 0.0006975932584312688, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:02<00:07, 3.62s/it] 100%|█████████▉| 519/520 [32:06<00:03, 3.60s/it] {'loss': 1.1395, 'grad_norm': 0.0006404947800095952, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:06<00:03, 3.60s/it] 100%|██████████| 520/520 [32:10<00:00, 3.86s/it] {'loss': 1.1291, 'grad_norm': 0.0006805037649971142, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:10<00:00, 3.86s/it] {'train_runtime': 1930.9578, 'train_samples_per_second': 34.454, 'train_steps_per_second': 0.269, 'train_loss': 1.217641509954746, 'epoch': 1.0} + 100%|██████████| 520/520 [32:10<00:00, 3.86s/it] 100%|██████████| 520/520 [32:10<00:00, 3.71s/it] +[2025-10-17 22:32:18,463] [INFO] [launch.py:348:main] Process 701572 exits successfully. +[2025-10-17 22:32:18,463] [INFO] [launch.py:348:main] Process 701574 exits successfully. +[2025-10-17 22:32:18,464] [INFO] [launch.py:348:main] Process 701575 exits successfully. +[2025-10-17 22:32:19,465] [INFO] [launch.py:348:main] Process 701577 exits successfully. +[2025-10-17 22:32:19,466] [INFO] [launch.py:348:main] Process 701573 exits successfully. +[2025-10-17 22:32:19,466] [INFO] [launch.py:348:main] Process 701578 exits successfully. +[2025-10-17 22:32:19,466] [INFO] [launch.py:348:main] Process 701576 exits successfully. +[2025-10-17 22:32:22,470] [INFO] [launch.py:348:main] Process 701571 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.1_2e-1_connector-7.0_2.1_2e-1_ablation_20251017_215834.log +Timestamp: 2025-10-17 22:32:25 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation_20251017_223225.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation_20251017_223225.log new file mode 100644 index 0000000000000000000000000000000000000000..2dfb980c16e12b9ffb79c1e0d38b4d7b9ce0e030 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation_20251017_223225.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation_20251017_223225.log +Timestamp: 2025-10-17 22:32:25 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 22:32:27,741] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:30,460] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 22:32:30,461] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 2.3 --temperature_mlp_text 2.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 2.3 --temperature_mlp_vision 2.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 2.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 22:32:33,029] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:34,072] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 22:32:34,072] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 22:32:34,072] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 22:32:34,072] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 22:32:34,072] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 22:32:34,072] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 22:32:34,072] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 22:32:34,074] [INFO] [launch.py:253:main] process 723258 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,076] [INFO] [launch.py:253:main] process 723259 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,078] [INFO] [launch.py:253:main] process 723260 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,080] [INFO] [launch.py:253:main] process 723261 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,082] [INFO] [launch.py:253:main] process 723262 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,084] [INFO] [launch.py:253:main] process 723263 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,086] [INFO] [launch.py:253:main] process 723264 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 22:32:34,087] [INFO] [launch.py:253:main] process 723265 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 22:32:40,937] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:40,977] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:40,978] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,015] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,015] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,017] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,019] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,030] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 22:32:41,351] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,379] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,381] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,422] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,423] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,425] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,427] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 22:32:41,427] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 22:32:41,441] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.3, 'temperature_mlp': 2.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: Apply masks for the following modules: ['llm', 'connector']['llm', 'connector'] + +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.3, + "temperature_mlp": 2.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:723258:723258 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723259:723259 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723263:723263 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723264:723264 [6] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723261:723261 [3] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723260:723260 [2] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:723262:723262 [4] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Using network Socket +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO ncclCommInitRank comm 0x557f51f97420 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO ncclCommInitRank comm 0x55ee60619af0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO ncclCommInitRank comm 0x55c4b9364d40 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO ncclCommInitRank comm 0x5587309fbdc0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO ncclCommInitRank comm 0x56049e8df030 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO ncclCommInitRank comm 0x559cfa380550 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO ncclCommInitRank comm 0x556c7dd43e30 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO ncclCommInitRank comm 0x55c7c7bfadb0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x89f37a872b3e1cf1 - Init START +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Setting affinity for GPU 4 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO NVLS multicast support is not available on dev 4 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Setting affinity for GPU 7 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO NVLS multicast support is not available on dev 7 +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Setting affinity for GPU 2 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO NVLS multicast support is not available on dev 2 +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Setting affinity for GPU 3 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO NVLS multicast support is not available on dev 3 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Setting affinity for GPU 6 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO NVLS multicast support is not available on dev 1 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO NVLS multicast support is not available on dev 0 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO NVLS multicast support is not available on dev 6 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Setting affinity for GPU 5 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO NVLS multicast support is not available on dev 5 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO comm 0x5587309fbdc0 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO comm 0x55c4b9364d40 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO comm 0x559cfa380550 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO comm 0x56049e8df030 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO comm 0x55c7c7bfadb0 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO comm 0x556c7dd43e30 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO comm 0x55ee60619af0 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO comm 0x557f51f97420 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723261:724908 [3] NCCL INFO ncclCommInitRank comm 0x559cfa380550 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723260:724909 [2] NCCL INFO ncclCommInitRank comm 0x55c7c7bfadb0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723258:724904 [0] NCCL INFO ncclCommInitRank comm 0x55ee60619af0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723264:724907 [6] NCCL INFO ncclCommInitRank comm 0x5587309fbdc0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:723259:724905 [1] NCCL INFO ncclCommInitRank comm 0x556c7dd43e30 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723265:724911 [7] NCCL INFO ncclCommInitRank comm 0x557f51f97420 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723262:724910 [4] NCCL INFO ncclCommInitRank comm 0x56049e8df030 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:723263:724906 [5] NCCL INFO ncclCommInitRank comm 0x55c4b9364d40 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x89f37a872b3e1cf1 - Init COMPLETE +[2025-10-17 22:33:25,680] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 22:33:27,471] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 22:33:45,950 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 22:33:45,957 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters 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+language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:001->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723258:729806 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723265:729812 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723260:729811 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723261:729813 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723259:729807 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723264:729810 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723263:729809 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:723262:729808 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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0xaef66fe0ab3876f8 - Init COMPLETE + 0%| | 1/520 [00:26<3:46:27, 26.18s/it] {'loss': 2.0759, 'grad_norm': 0.01124516206346042, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:26<3:46:27, 26.18s/it] 0%| | 2/520 [00:29<1:51:29, 12.91s/it] {'loss': 2.0791, 'grad_norm': 0.01215805062727419, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:29<1:51:29, 12.91s/it] 1%| | 3/520 [00:33<1:14:35, 8.66s/it] {'loss': 2.2241, 'grad_norm': 0.01389958013676167, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:33<1:14:35, 8.66s/it] 1%| | 4/520 [00:37<57:19, 6.67s/it] {'loss': 1.6604, 'grad_norm': 0.004508801083526778, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:37<57:19, 6.67s/it] 1%| | 5/520 [00:40<48:15, 5.62s/it] {'loss': 1.673, 'grad_norm': 0.002851378666437558, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:40<48:15, 5.62s/it] 1%| | 6/520 [00:44<42:53, 5.01s/it] {'loss': 1.3924, 'grad_norm': 0.0013254342283928104, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:44<42:53, 5.01s/it] 1%|▏ | 7/520 [00:48<39:19, 4.60s/it] {'loss': 1.4262, 'grad_norm': 0.001594090108064308, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:48<39:19, 4.60s/it] 2%|▏ | 8/520 [00:52<38:45, 4.54s/it] {'loss': 1.4651, 'grad_norm': 0.001479687529388167, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:52<38:45, 4.54s/it] 2%|▏ | 9/520 [00:56<36:37, 4.30s/it] {'loss': 1.5338, 'grad_norm': 0.0013938343695734465, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:56<36:37, 4.30s/it] 2%|▏ | 10/520 [01:00<35:08, 4.13s/it] {'loss': 1.3722, 'grad_norm': 0.0013308690860322912, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [01:00<35:08, 4.13s/it] 2%|▏ | 11/520 [01:04<34:02, 4.01s/it] {'loss': 1.4273, 'grad_norm': 0.0010152855738268965, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [01:04<34:02, 4.01s/it] 2%|▏ | 12/520 [01:07<32:55, 3.89s/it] {'loss': 1.3226, 'grad_norm': 0.001131815447427121, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:07<32:55, 3.89s/it][2025-10-17 22:35:03,288] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:11<33:54, 4.01s/it] {'loss': 1.3712, 'grad_norm': 0.001256757616184834, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:11<33:54, 4.01s/it] 3%|▎ | 14/520 [01:15<32:42, 3.88s/it] {'loss': 1.4225, 'grad_norm': 0.0014120938268601022, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:15<32:42, 3.88s/it] 3%|▎ | 15/520 [01:19<31:52, 3.79s/it] {'loss': 1.3701, 'grad_norm': 0.0009578852784666649, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:19<31:52, 3.79s/it] 3%|▎ | 16/520 [01:22<31:18, 3.73s/it] {'loss': 1.3287, 'grad_norm': 0.0010008694027592532, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:22<31:18, 3.73s/it] 3%|▎ | 17/520 [01:26<30:58, 3.70s/it] {'loss': 1.4466, 'grad_norm': 0.0010736378385169372, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:26<30:58, 3.70s/it] 3%|▎ | 18/520 [01:29<30:40, 3.67s/it] {'loss': 1.3017, 'grad_norm': 0.0012477600181935472, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:29<30:40, 3.67s/it] 4%|▎ | 19/520 [01:33<30:29, 3.65s/it] {'loss': 1.3279, 'grad_norm': 0.000960980055130603, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:33<30:29, 3.65s/it] 4%|▍ | 20/520 [01:37<30:19, 3.64s/it] {'loss': 1.2857, 'grad_norm': 0.0012085495438934775, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:37<30:19, 3.64s/it] 4%|▍ | 21/520 [01:40<30:15, 3.64s/it] {'loss': 1.3333, 'grad_norm': 0.0013117245040732894, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:40<30:15, 3.64s/it] 4%|▍ | 22/520 [01:44<30:09, 3.63s/it] {'loss': 1.432, 'grad_norm': 0.0009474522340119628, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:44<30:09, 3.63s/it] 4%|▍ | 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'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [19:07<13:31, 3.74s/it] 58%|█████▊ | 304/520 [19:11<13:54, 3.86s/it] {'loss': 1.136, 'grad_norm': 0.0008016238167697155, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:11<13:54, 3.86s/it] 59%|█████▊ | 305/520 [19:15<13:43, 3.83s/it] {'loss': 1.2735, 'grad_norm': 0.0008465208267694317, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:15<13:43, 3.83s/it] 59%|█████▉ | 306/520 [19:19<13:40, 3.83s/it] {'loss': 1.2199, 'grad_norm': 0.0007626055268088407, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:19<13:40, 3.83s/it] 59%|█████▉ | 307/520 [19:23<13:34, 3.83s/it] {'loss': 1.164, 'grad_norm': 0.0007272441349652488, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:23<13:34, 3.83s/it] 59%|█████▉ | 308/520 [19:27<13:29, 3.82s/it] {'loss': 1.2748, 'grad_norm': 0.0008306014908314312, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:27<13:29, 3.82s/it] 59%|█████▉ | 309/520 [19:30<13:22, 3.80s/it] {'loss': 1.1669, 'grad_norm': 0.0007255912293492949, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:30<13:22, 3.80s/it] 60%|█████▉ | 310/520 [19:34<13:17, 3.80s/it] {'loss': 1.1467, 'grad_norm': 0.0007386021551410665, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:34<13:17, 3.80s/it] 60%|█████▉ | 311/520 [19:38<13:12, 3.79s/it] {'loss': 1.1235, 'grad_norm': 0.0007429315921926758, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:38<13:12, 3.79s/it] 60%|██████ | 312/520 [19:42<13:10, 3.80s/it] {'loss': 1.1154, 'grad_norm': 0.0007514874363983343, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:42<13:10, 3.80s/it] 60%|██████ | 313/520 [19:46<13:05, 3.80s/it] {'loss': 1.1002, 'grad_norm': 0.0006831166624056002, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:46<13:05, 3.80s/it] 60%|██████ | 314/520 [19:50<13:24, 3.90s/it] {'loss': 1.1382, 'grad_norm': 0.0007177634908017572, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:50<13:24, 3.90s/it] 61%|██████ | 315/520 [19:54<13:11, 3.86s/it] {'loss': 1.1817, 'grad_norm': 0.0009520821824949319, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:54<13:11, 3.86s/it] 61%|██████ | 316/520 [19:58<13:27, 3.96s/it] {'loss': 1.121, 'grad_norm': 0.0007984030906038413, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:58<13:27, 3.96s/it] 61%|██████ | 317/520 [20:01<13:13, 3.91s/it] {'loss': 1.1266, 'grad_norm': 0.0006409917090113541, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [20:02<13:13, 3.91s/it] 61%|██████ | 318/520 [20:05<13:02, 3.87s/it] {'loss': 1.2357, 'grad_norm': 0.0007919061680229586, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [20:05<13:02, 3.87s/it] 61%|██████▏ | 319/520 [20:09<13:17, 3.97s/it] {'loss': 1.1175, 'grad_norm': 0.0006509011850518756, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:09<13:17, 3.97s/it] 62%|██████▏ | 320/520 [20:13<13:01, 3.91s/it] {'loss': 1.0656, 'grad_norm': 0.0007558268319176335, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:13<13:01, 3.91s/it] 62%|██████▏ | 321/520 [20:17<12:48, 3.86s/it] {'loss': 1.2579, 'grad_norm': 0.0007982443287727196, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:17<12:48, 3.86s/it] 62%|██████▏ | 322/520 [20:21<12:28, 3.78s/it] {'loss': 1.0843, 'grad_norm': 0.0006809176117053533, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:21<12:28, 3.78s/it] 62%|██████▏ | 323/520 [20:24<12:15, 3.73s/it] {'loss': 1.153, 'grad_norm': 0.0007034912542200882, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:24<12:15, 3.73s/it] 62%|██████▏ | 324/520 [20:28<12:02, 3.69s/it] {'loss': 1.2023, 'grad_norm': 0.0007437013773177541, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:28<12:02, 3.69s/it] 62%|██████▎ | 325/520 [20:31<11:56, 3.67s/it] {'loss': 1.1996, 'grad_norm': 0.0007895999489413851, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:31<11:56, 3.67s/it] 63%|██████▎ | 326/520 [20:35<11:48, 3.65s/it] {'loss': 1.1988, 'grad_norm': 0.0007408718961059894, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:35<11:48, 3.65s/it] 63%|██████▎ | 327/520 [20:39<11:51, 3.69s/it] {'loss': 1.1845, 'grad_norm': 0.0007291082723448026, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:39<11:51, 3.69s/it] 63%|██████▎ | 328/520 [20:43<11:53, 3.72s/it] {'loss': 1.2377, 'grad_norm': 0.0007933543710213717, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:43<11:53, 3.72s/it] 63%|██████▎ | 329/520 [20:46<11:53, 3.74s/it] {'loss': 1.1243, 'grad_norm': 0.0006431334880418636, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:46<11:53, 3.74s/it] 63%|██████▎ | 330/520 [20:50<11:52, 3.75s/it] {'loss': 1.1944, 'grad_norm': 0.0006861754543998912, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:50<11:52, 3.75s/it] 64%|██████▎ | 331/520 [20:54<12:09, 3.86s/it] {'loss': 1.1565, 'grad_norm': 0.0007245710401792413, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:54<12:09, 3.86s/it] 64%|██████▍ | 332/520 [20:58<12:01, 3.84s/it] {'loss': 1.2146, 'grad_norm': 0.0006530449031665299, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:58<12:01, 3.84s/it] 64%|██████▍ | 333/520 [21:02<11:56, 3.83s/it] {'loss': 1.2899, 'grad_norm': 0.0007883429879701382, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [21:02<11:56, 3.83s/it] 64%|██████▍ | 334/520 [21:06<11:51, 3.83s/it] {'loss': 1.2016, 'grad_norm': 0.0007729876207504195, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [21:06<11:51, 3.83s/it] 64%|██████▍ | 335/520 [21:10<11:47, 3.83s/it] {'loss': 1.2029, 'grad_norm': 0.0006901788827224837, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:10<11:47, 3.83s/it] 65%|██████▍ | 336/520 [21:13<11:34, 3.78s/it] {'loss': 1.1084, 'grad_norm': 0.0007968235006721235, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:13<11:34, 3.78s/it] 65%|██████▍ | 337/520 [21:17<11:21, 3.73s/it] {'loss': 1.0986, 'grad_norm': 0.0007187754127064978, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:17<11:21, 3.73s/it] 65%|██████▌ | 338/520 [21:20<11:11, 3.69s/it] {'loss': 1.2039, 'grad_norm': 0.0007178429205009906, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:20<11:11, 3.69s/it] 65%|██████▌ | 339/520 [21:24<11:03, 3.66s/it] {'loss': 1.152, 'grad_norm': 0.0007351340428979128, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:24<11:03, 3.66s/it] 65%|██████▌ | 340/520 [21:28<10:57, 3.65s/it] {'loss': 1.1421, 'grad_norm': 0.0007266958180673412, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:28<10:57, 3.65s/it] 66%|██████▌ | 341/520 [21:31<10:51, 3.64s/it] {'loss': 1.1673, 'grad_norm': 0.0007592431296925607, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:31<10:51, 3.64s/it] 66%|██████▌ | 342/520 [21:35<10:45, 3.62s/it] {'loss': 1.1883, 'grad_norm': 0.0008564375900115046, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:35<10:45, 3.62s/it] 66%|██████▌ | 343/520 [21:38<10:40, 3.62s/it] {'loss': 1.1401, 'grad_norm': 0.0007545515404002318, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:38<10:40, 3.62s/it] 66%|██████▌ | 344/520 [21:42<10:36, 3.62s/it] {'loss': 1.1255, 'grad_norm': 0.0007045494427907116, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:42<10:36, 3.62s/it] 66%|██████▋ | 345/520 [21:46<10:32, 3.61s/it] {'loss': 1.2264, 'grad_norm': 0.0007546180554664258, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:46<10:32, 3.61s/it] 67%|██████▋ | 346/520 [21:49<10:28, 3.61s/it] {'loss': 1.1624, 'grad_norm': 0.0007684301540444658, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:49<10:28, 3.61s/it] 67%|██████▋ | 347/520 [21:53<10:24, 3.61s/it] {'loss': 1.1412, 'grad_norm': 0.0006686294680339475, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:53<10:24, 3.61s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:56<10:22, 3.62s/it] {'loss': 1.0973, 'grad_norm': 0.0009749674008021876, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:56<10:22, 3.62s/it] 67%|██████▋ | 349/520 [22:00<10:18, 3.61s/it] {'loss': 1.1358, 'grad_norm': 0.0007071437270165497, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [22:00<10:18, 3.61s/it] 67%|██████▋ | 350/520 [22:04<10:15, 3.62s/it] {'loss': 1.1792, 'grad_norm': 0.000850060178217297, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [22:04<10:15, 3.62s/it] 68%|██████▊ | 351/520 [22:07<10:12, 3.62s/it] {'loss': 1.0894, 'grad_norm': 0.000669792176091483, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:07<10:12, 3.62s/it] 68%|██████▊ | 352/520 [22:11<10:07, 3.62s/it] {'loss': 1.2049, 'grad_norm': 0.0006855100570784392, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:11<10:07, 3.62s/it] 68%|██████▊ | 353/520 [22:15<10:08, 3.64s/it] {'loss': 1.1282, 'grad_norm': 0.0006278463195840064, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:15<10:08, 3.64s/it] 68%|██████▊ | 354/520 [22:18<10:01, 3.63s/it] {'loss': 1.2244, 'grad_norm': 0.0006636250023977442, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:18<10:01, 3.63s/it] 68%|██████▊ | 355/520 [22:22<09:58, 3.63s/it] {'loss': 1.1504, 'grad_norm': 0.0007019448906358652, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:22<09:58, 3.63s/it] 68%|██████▊ | 356/520 [22:25<09:53, 3.62s/it] {'loss': 1.1521, 'grad_norm': 0.0007282071416584225, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:25<09:53, 3.62s/it] 69%|██████▊ | 357/520 [22:29<09:48, 3.61s/it] {'loss': 1.1842, 'grad_norm': 0.0006744137435494421, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:29<09:48, 3.61s/it] 69%|██████▉ | 358/520 [22:33<09:45, 3.62s/it] {'loss': 1.115, 'grad_norm': 0.0007165468024549518, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:33<09:45, 3.62s/it] 69%|██████▉ | 359/520 [22:36<09:42, 3.62s/it] {'loss': 1.1652, 'grad_norm': 0.0007510903563489726, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:36<09:42, 3.62s/it] 69%|██████▉ | 360/520 [22:40<09:38, 3.62s/it] {'loss': 1.1737, 'grad_norm': 0.0007637349893433175, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:40<09:38, 3.62s/it] 69%|██████▉ | 361/520 [22:44<09:35, 3.62s/it] {'loss': 1.1883, 'grad_norm': 0.0006715472922793017, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:44<09:35, 3.62s/it] 70%|██████▉ | 362/520 [22:47<09:31, 3.62s/it] {'loss': 1.1641, 'grad_norm': 0.000772473633967655, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:47<09:31, 3.62s/it] 70%|██████▉ | 363/520 [22:51<09:27, 3.62s/it] {'loss': 1.1886, 'grad_norm': 0.000709512247848098, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:51<09:27, 3.62s/it] 70%|███████ | 364/520 [22:55<09:32, 3.67s/it] {'loss': 1.204, 'grad_norm': 0.0007173120451951879, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:55<09:32, 3.67s/it] 70%|███████ | 365/520 [22:58<09:36, 3.72s/it] {'loss': 1.2439, 'grad_norm': 0.000749734765945557, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:58<09:36, 3.72s/it] 70%|███████ | 366/520 [23:02<09:40, 3.77s/it] {'loss': 1.2055, 'grad_norm': 0.0006900620949957137, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [23:02<09:40, 3.77s/it] 71%|███████ | 367/520 [23:06<09:40, 3.79s/it] {'loss': 1.2051, 'grad_norm': 0.0007242123653705194, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:06<09:40, 3.79s/it] 71%|███████ | 368/520 [23:10<09:39, 3.81s/it] {'loss': 1.0596, 'grad_norm': 0.0007387713583057017, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:10<09:39, 3.81s/it] 71%|███████ | 369/520 [23:14<09:39, 3.84s/it] {'loss': 1.1631, 'grad_norm': 0.0006413604978181116, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:14<09:39, 3.84s/it] 71%|███████ | 370/520 [23:18<09:34, 3.83s/it] {'loss': 1.1205, 'grad_norm': 0.0006888197021212331, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:18<09:34, 3.83s/it] 71%|███████▏ | 371/520 [23:22<09:30, 3.83s/it] {'loss': 1.1177, 'grad_norm': 0.0007663855399311902, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:22<09:30, 3.83s/it] 72%|███████▏ | 372/520 [23:25<09:27, 3.84s/it] {'loss': 1.2323, 'grad_norm': 0.000767939070276247, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:25<09:27, 3.84s/it] 72%|███████▏ | 373/520 [23:29<09:24, 3.84s/it] {'loss': 1.1227, 'grad_norm': 0.0007441453869550706, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:29<09:24, 3.84s/it] 72%|███████▏ | 374/520 [23:33<09:20, 3.84s/it] {'loss': 1.207, 'grad_norm': 0.0007336356651069874, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:33<09:20, 3.84s/it] 72%|███████▏ | 375/520 [23:37<09:17, 3.84s/it] {'loss': 1.1246, 'grad_norm': 0.0007477118272615367, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:37<09:17, 3.84s/it] 72%|███████▏ | 376/520 [23:41<09:13, 3.85s/it] {'loss': 1.231, 'grad_norm': 0.0006854989121251914, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:41<09:13, 3.85s/it] 72%|███████▎ | 377/520 [23:45<09:10, 3.85s/it] {'loss': 1.1607, 'grad_norm': 0.000726191786474057, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:45<09:10, 3.85s/it] 73%|███████▎ | 378/520 [23:49<09:06, 3.85s/it] {'loss': 1.2264, 'grad_norm': 0.0006884487265941401, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:49<09:06, 3.85s/it] 73%|███████▎ | 379/520 [23:52<08:53, 3.78s/it] {'loss': 1.1962, 'grad_norm': 0.0006869008382094, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:52<08:53, 3.78s/it] 73%|███████▎ | 380/520 [23:56<08:42, 3.73s/it] {'loss': 1.2096, 'grad_norm': 0.0007009365344365108, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:56<08:42, 3.73s/it] 73%|███████▎ | 381/520 [23:59<08:34, 3.70s/it] {'loss': 1.2033, 'grad_norm': 0.0006807601942264514, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:59<08:34, 3.70s/it] 73%|███████▎ | 382/520 [24:03<08:30, 3.70s/it] {'loss': 1.18, 'grad_norm': 0.0006692069368707948, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [24:03<08:30, 3.70s/it] 74%|███████▎ | 383/520 [24:07<08:24, 3.68s/it] {'loss': 1.0439, 'grad_norm': 0.0008202070099933615, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [24:07<08:24, 3.68s/it] 74%|███████▍ | 384/520 [24:10<08:20, 3.68s/it] {'loss': 1.2081, 'grad_norm': 0.0006539186220900853, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:10<08:20, 3.68s/it] 74%|███████▍ | 385/520 [24:14<08:14, 3.67s/it] {'loss': 1.1829, 'grad_norm': 0.0006624722263669189, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:14<08:14, 3.67s/it] 74%|███████▍ | 386/520 [24:18<08:10, 3.66s/it] {'loss': 1.1391, 'grad_norm': 0.0006403911290416501, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:18<08:10, 3.66s/it] 74%|███████▍ | 387/520 [24:21<08:06, 3.65s/it] {'loss': 1.2323, 'grad_norm': 0.0007269173656582971, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:21<08:06, 3.65s/it] 75%|███████▍ | 388/520 [24:25<08:01, 3.65s/it] {'loss': 1.0963, 'grad_norm': 0.0006935829654680324, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:25<08:01, 3.65s/it] 75%|███████▍ | 389/520 [24:29<07:56, 3.64s/it] {'loss': 1.1415, 'grad_norm': 0.0008520458125060357, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:29<07:56, 3.64s/it] 75%|███████▌ | 390/520 [24:32<07:53, 3.64s/it] {'loss': 1.2059, 'grad_norm': 0.0007002569757147547, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:32<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:36<07:49, 3.64s/it] {'loss': 1.2732, 'grad_norm': 0.0007630140225803764, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:36<07:49, 3.64s/it] 75%|███████▌ | 392/520 [24:39<07:45, 3.64s/it] {'loss': 1.098, 'grad_norm': 0.0007679795526758074, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:39<07:45, 3.64s/it] 76%|███████▌ | 393/520 [24:43<07:40, 3.63s/it] {'loss': 1.0891, 'grad_norm': 0.000584151410025618, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:43<07:40, 3.63s/it] 76%|███████▌ | 394/520 [24:47<07:35, 3.62s/it] {'loss': 1.1644, 'grad_norm': 0.0007440700416045319, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:47<07:35, 3.62s/it] 76%|███████▌ | 395/520 [24:50<07:32, 3.62s/it] {'loss': 1.1276, 'grad_norm': 0.0007827119622229478, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:50<07:32, 3.62s/it] 76%|███████▌ | 396/520 [24:54<07:27, 3.61s/it] {'loss': 1.2087, 'grad_norm': 0.0007717975617165646, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:54<07:27, 3.61s/it] 76%|███████▋ | 397/520 [24:58<07:24, 3.62s/it] {'loss': 1.184, 'grad_norm': 0.0006804799466606696, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:58<07:24, 3.62s/it] 77%|███████▋ | 398/520 [25:01<07:20, 3.61s/it] {'loss': 1.1822, 'grad_norm': 0.0007463472967867477, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [25:01<07:20, 3.61s/it] 77%|███████▋ | 399/520 [25:05<07:17, 3.62s/it] {'loss': 1.1231, 'grad_norm': 0.0006694646798685918, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [25:05<07:17, 3.62s/it] 77%|███████▋ | 400/520 [25:08<07:17, 3.64s/it] {'loss': 1.1546, 'grad_norm': 0.0006595160686280585, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:08<07:17, 3.64s/it] 77%|███████▋ | 401/520 [25:12<07:13, 3.64s/it] {'loss': 1.0197, 'grad_norm': 0.0007726610735478901, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:12<07:13, 3.64s/it] 77%|███████▋ | 402/520 [25:16<07:16, 3.70s/it] {'loss': 1.1483, 'grad_norm': 0.0007194825086527847, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:16<07:16, 3.70s/it] 78%|███████▊ | 403/520 [25:20<07:17, 3.74s/it] {'loss': 1.1684, 'grad_norm': 0.0007812895411255001, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:20<07:17, 3.74s/it] 78%|███████▊ | 404/520 [25:24<07:15, 3.76s/it] {'loss': 1.0792, 'grad_norm': 0.0008179781264673322, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:24<07:15, 3.76s/it] 78%|███████▊ | 405/520 [25:27<07:12, 3.76s/it] {'loss': 1.1384, 'grad_norm': 0.0007559942670171749, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:27<07:12, 3.76s/it] 78%|███████▊ | 406/520 [25:31<07:10, 3.77s/it] {'loss': 1.0531, 'grad_norm': 0.0009792654631763894, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:31<07:10, 3.77s/it] 78%|███████▊ | 407/520 [25:35<07:07, 3.79s/it] {'loss': 1.246, 'grad_norm': 0.0007785308688793588, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:35<07:07, 3.79s/it] 78%|███████▊ | 408/520 [25:39<07:04, 3.79s/it] {'loss': 1.1625, 'grad_norm': 0.0009287909587607687, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:39<07:04, 3.79s/it] 79%|███████▊ | 409/520 [25:43<07:01, 3.80s/it] {'loss': 1.274, 'grad_norm': 0.0007991430438483377, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:43<07:01, 3.80s/it] 79%|███████▉ | 410/520 [25:46<06:57, 3.80s/it] {'loss': 1.0191, 'grad_norm': 0.0007516449253332056, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:46<06:57, 3.80s/it] 79%|███████▉ | 411/520 [25:50<06:54, 3.80s/it] {'loss': 1.2563, 'grad_norm': 0.0007733913118075364, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:50<06:54, 3.80s/it] 79%|███████▉ | 412/520 [25:54<06:51, 3.81s/it] {'loss': 1.1659, 'grad_norm': 0.0007035988603038919, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:54<06:51, 3.81s/it] 79%|███████▉ | 413/520 [25:58<06:47, 3.81s/it] {'loss': 1.1476, 'grad_norm': 0.0006853757931597652, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:58<06:47, 3.81s/it] 80%|███████▉ | 414/520 [26:02<06:43, 3.81s/it] {'loss': 0.963, 'grad_norm': 0.0005879420914568221, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [26:02<06:43, 3.81s/it] 80%|███████▉ | 415/520 [26:05<06:40, 3.81s/it] {'loss': 1.1487, 'grad_norm': 0.0006741020426221982, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [26:05<06:40, 3.81s/it] 80%|████████ | 416/520 [26:09<06:36, 3.81s/it] {'loss': 1.0585, 'grad_norm': 0.0007898806291486042, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:09<06:36, 3.81s/it] 80%|████████ | 417/520 [26:13<06:32, 3.82s/it] {'loss': 1.2199, 'grad_norm': 0.0007436029713134124, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:13<06:32, 3.82s/it] 80%|████████ | 418/520 [26:17<06:26, 3.79s/it] {'loss': 1.2111, 'grad_norm': 0.0007430027577671886, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:17<06:26, 3.79s/it] 81%|████████ | 419/520 [26:20<06:17, 3.74s/it] {'loss': 1.2039, 'grad_norm': 0.0007710691281552972, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:20<06:17, 3.74s/it] 81%|████████ | 420/520 [26:24<06:11, 3.71s/it] {'loss': 1.0975, 'grad_norm': 0.0007634334861857855, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:24<06:11, 3.71s/it] 81%|████████ | 421/520 [26:28<06:05, 3.69s/it] {'loss': 1.0332, 'grad_norm': 0.0008346474240052908, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:28<06:05, 3.69s/it] 81%|████████ | 422/520 [26:31<05:59, 3.67s/it] {'loss': 1.1536, 'grad_norm': 0.0007492938404354536, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:31<05:59, 3.67s/it] 81%|████████▏ | 423/520 [26:35<05:54, 3.65s/it] {'loss': 1.1247, 'grad_norm': 0.0007748504412918336, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:35<05:54, 3.65s/it] 82%|████████▏ | 424/520 [26:39<05:50, 3.65s/it] {'loss': 1.2322, 'grad_norm': 0.0007021221206754657, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:39<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:42<05:45, 3.64s/it] {'loss': 1.1441, 'grad_norm': 0.0006869346324085506, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:42<05:45, 3.64s/it] 82%|████████▏ | 426/520 [26:46<05:41, 3.63s/it] {'loss': 1.1678, 'grad_norm': 0.0009341982830666132, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:46<05:41, 3.63s/it] 82%|████████▏ | 427/520 [26:49<05:36, 3.62s/it] {'loss': 1.0768, 'grad_norm': 0.0006896700348906602, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:49<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:53<05:32, 3.62s/it] {'loss': 1.0642, 'grad_norm': 0.0007630731293434524, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:53<05:32, 3.62s/it] 82%|████████▎ | 429/520 [26:57<05:30, 3.63s/it] {'loss': 1.1577, 'grad_norm': 0.0007700587218391008, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:57<05:30, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [27:00<05:27, 3.64s/it] {'loss': 1.1585, 'grad_norm': 0.0006636237432027565, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [27:00<05:27, 3.64s/it] 83%|████████▎ | 431/520 [27:04<05:23, 3.63s/it] {'loss': 1.1219, 'grad_norm': 0.0007687493166762126, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [27:04<05:23, 3.63s/it] 83%|████████▎ | 432/520 [27:08<05:19, 3.63s/it] {'loss': 1.0694, 'grad_norm': 0.0007246594184251324, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:08<05:19, 3.63s/it] 83%|████████▎ | 433/520 [27:11<05:15, 3.62s/it] {'loss': 1.1995, 'grad_norm': 0.0007465517639161545, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:11<05:15, 3.62s/it] 83%|████████▎ | 434/520 [27:15<05:13, 3.65s/it] {'loss': 0.9504, 'grad_norm': 0.0006991628089470225, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:15<05:13, 3.65s/it] 84%|████████▎ | 435/520 [27:18<05:08, 3.63s/it] {'loss': 1.2312, 'grad_norm': 0.0007914651111890637, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:18<05:08, 3.63s/it] 84%|████████▍ | 436/520 [27:22<05:05, 3.63s/it] {'loss': 1.0407, 'grad_norm': 0.0007532908607046644, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:22<05:05, 3.63s/it] 84%|████████▍ | 437/520 [27:26<05:01, 3.63s/it] {'loss': 1.2519, 'grad_norm': 0.0007271716085060902, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:26<05:01, 3.63s/it] 84%|████████▍ | 438/520 [27:29<04:56, 3.62s/it] {'loss': 1.0788, 'grad_norm': 0.0007369466411656922, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:29<04:56, 3.62s/it] 84%|████████▍ | 439/520 [27:33<04:53, 3.63s/it] {'loss': 1.105, 'grad_norm': 0.0005854177048396905, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:33<04:53, 3.63s/it] 85%|████████▍ | 440/520 [27:37<04:49, 3.62s/it] {'loss': 1.1089, 'grad_norm': 0.0007557378623226799, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:37<04:49, 3.62s/it] 85%|████████▍ | 441/520 [27:40<04:47, 3.64s/it] {'loss': 1.1173, 'grad_norm': 0.0007365411725179693, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:40<04:47, 3.64s/it] 85%|████████▌ | 442/520 [27:44<04:44, 3.64s/it] {'loss': 1.1726, 'grad_norm': 0.0007845553947161529, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:44<04:44, 3.64s/it] 85%|████████▌ | 443/520 [27:48<04:39, 3.63s/it] {'loss': 1.1857, 'grad_norm': 0.0006987694523966033, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:48<04:39, 3.63s/it] 85%|████████▌ | 444/520 [27:51<04:36, 3.64s/it] {'loss': 1.1502, 'grad_norm': 0.0006475784525672509, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:51<04:36, 3.64s/it] 86%|████████▌ | 445/520 [27:55<04:32, 3.63s/it] {'loss': 1.0788, 'grad_norm': 0.0006998036226732585, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:55<04:32, 3.63s/it] 86%|████████▌ | 446/520 [27:58<04:28, 3.63s/it] {'loss': 1.1944, 'grad_norm': 0.0006428816162263051, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:58<04:28, 3.63s/it] 86%|████████▌ | 447/520 [28:02<04:25, 3.64s/it] {'loss': 1.1515, 'grad_norm': 0.0006869424478323611, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [28:02<04:25, 3.64s/it] 86%|████████▌ | 448/520 [28:06<04:21, 3.63s/it] {'loss': 1.1483, 'grad_norm': 0.0007469038538076959, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [28:06<04:21, 3.63s/it] 86%|████████▋ | 449/520 [28:09<04:17, 3.63s/it] {'loss': 1.1547, 'grad_norm': 0.0007116050884380174, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:09<04:17, 3.63s/it] 87%|████████▋ | 450/520 [28:13<04:13, 3.63s/it] {'loss': 1.1737, 'grad_norm': 0.0007128016977862786, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:13<04:13, 3.63s/it] 87%|████████▋ | 451/520 [28:17<04:09, 3.62s/it] {'loss': 1.177, 'grad_norm': 0.0007307168184784822, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:17<04:09, 3.62s/it] 87%|████████▋ | 452/520 [28:20<04:06, 3.62s/it] {'loss': 1.1985, 'grad_norm': 0.0006811542643029872, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:20<04:06, 3.62s/it] 87%|████████▋ | 453/520 [28:24<04:03, 3.64s/it] {'loss': 1.1746, 'grad_norm': 0.0006791315487135773, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:24<04:03, 3.64s/it] 87%|████████▋ | 454/520 [28:27<03:59, 3.63s/it] {'loss': 1.0844, 'grad_norm': 0.000724413276204591, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:27<03:59, 3.63s/it] 88%|████████▊ | 455/520 [28:31<03:56, 3.64s/it] {'loss': 1.2258, 'grad_norm': 0.0007080949382647497, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:31<03:56, 3.64s/it] 88%|████████▊ | 456/520 [28:35<03:51, 3.62s/it] {'loss': 1.1565, 'grad_norm': 0.0007275233908835685, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:35<03:51, 3.62s/it] 88%|████████▊ | 457/520 [28:38<03:48, 3.62s/it] {'loss': 1.0684, 'grad_norm': 0.0006083710775380818, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:38<03:48, 3.62s/it] 88%|████████▊ | 458/520 [28:42<03:44, 3.62s/it] {'loss': 1.2739, 'grad_norm': 0.0008029788030741219, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:42<03:44, 3.62s/it] 88%|████████▊ | 459/520 [28:46<03:40, 3.62s/it] {'loss': 1.2082, 'grad_norm': 0.0007973024875214381, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:46<03:40, 3.62s/it] 88%|████████▊ | 460/520 [28:49<03:36, 3.62s/it] {'loss': 1.1003, 'grad_norm': 0.0006955100779266958, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:49<03:36, 3.62s/it] 89%|████████▊ | 461/520 [28:53<03:33, 3.62s/it] {'loss': 1.1491, 'grad_norm': 0.0005414937664220687, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:53<03:33, 3.62s/it] 89%|████████▉ | 462/520 [28:56<03:29, 3.62s/it] {'loss': 1.2432, 'grad_norm': 0.0006674524781813832, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:56<03:29, 3.62s/it] 89%|████████▉ | 463/520 [29:00<03:26, 3.62s/it] {'loss': 1.0659, 'grad_norm': 0.0007800959551062336, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [29:00<03:26, 3.62s/it] 89%|████████▉ | 464/520 [29:04<03:23, 3.63s/it] {'loss': 1.1886, 'grad_norm': 0.00074857698454751, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [29:04<03:23, 3.63s/it] 89%|████████▉ | 465/520 [29:07<03:19, 3.62s/it] {'loss': 1.2908, 'grad_norm': 0.0007356394904394396, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [29:07<03:19, 3.62s/it] 90%|████████▉ | 466/520 [29:11<03:15, 3.62s/it] {'loss': 1.1843, 'grad_norm': 0.0007390532384173212, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:11<03:15, 3.62s/it] 90%|████████▉ | 467/520 [29:15<03:12, 3.63s/it] {'loss': 1.1311, 'grad_norm': 0.0006418604577281116, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:15<03:12, 3.63s/it] 90%|█████████ | 468/520 [29:18<03:09, 3.65s/it] {'loss': 1.1539, 'grad_norm': 0.0007897617062841922, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:18<03:09, 3.65s/it] 90%|█████████ | 469/520 [29:22<03:05, 3.64s/it] {'loss': 1.2212, 'grad_norm': 0.0008719805222032279, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:22<03:05, 3.64s/it] 90%|█████████ | 470/520 [29:26<03:01, 3.63s/it] {'loss': 1.0965, 'grad_norm': 0.0006527733959771532, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:26<03:01, 3.63s/it] 91%|█████████ | 471/520 [29:29<02:57, 3.63s/it] {'loss': 1.1205, 'grad_norm': 0.0007515478602651779, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:29<02:57, 3.63s/it] 91%|█████████ | 472/520 [29:33<02:54, 3.64s/it] {'loss': 1.0922, 'grad_norm': 0.0008096405974028205, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:33<02:54, 3.64s/it] 91%|█████████ | 473/520 [29:36<02:51, 3.64s/it] {'loss': 1.1591, 'grad_norm': 0.000775089782354957, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:36<02:51, 3.64s/it] 91%|█████████ | 474/520 [29:40<02:47, 3.64s/it] {'loss': 1.167, 'grad_norm': 0.0006604985987312386, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:40<02:47, 3.64s/it] 91%|█████████▏| 475/520 [29:44<02:43, 3.63s/it] {'loss': 1.0851, 'grad_norm': 0.00067963189053141, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:44<02:43, 3.63s/it] 92%|█████████▏| 476/520 [29:47<02:39, 3.62s/it] {'loss': 1.143, 'grad_norm': 0.0007257580474322337, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:47<02:39, 3.62s/it] 92%|█████████▏| 477/520 [29:51<02:35, 3.61s/it] {'loss': 1.1412, 'grad_norm': 0.0008183641229627458, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:51<02:35, 3.61s/it] 92%|█████████▏| 478/520 [29:55<02:31, 3.62s/it] {'loss': 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[30:14<02:24, 3.90s/it] {'loss': 1.1521, 'grad_norm': 0.0007384812699793489, 'learning_rate': 0.002647806273887665, 'epoch': 0.93} + 93%|█████████▎| 483/520 [30:14<02:24, 3.90s/it] 93%|█████████▎| 484/520 [30:18<02:22, 3.95s/it] {'loss': 1.1628, 'grad_norm': 0.0007435402042488204, 'learning_rate': 0.0025072087818176383, 'epoch': 0.93} + 93%|█████████▎| 484/520 [30:18<02:22, 3.95s/it] 93%|█████████▎| 485/520 [30:22<02:19, 3.98s/it] {'loss': 1.1145, 'grad_norm': 0.0006820149663988523, 'learning_rate': 0.002370399288006664, 'epoch': 0.93} + 93%|█████████▎| 485/520 [30:22<02:19, 3.98s/it] 93%|█████████▎| 486/520 [30:26<02:13, 3.93s/it] {'loss': 1.2354, 'grad_norm': 0.0007556480133192542, 'learning_rate': 0.0022373831080695463, 'epoch': 0.93} + 93%|█████████▎| 486/520 [30:26<02:13, 3.93s/it] 94%|█████████▎| 487/520 [30:30<02:08, 3.89s/it] {'loss': 1.093, 'grad_norm': 0.00082600812780409, 'learning_rate': 0.0021081654102351635, 'epoch': 0.94} + 94%|█████████▎| 487/520 [30:30<02:08, 3.89s/it] 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3.62s/it] {'loss': 1.0226, 'grad_norm': 0.0007704764303872411, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [32:03<00:28, 3.62s/it] 99%|█████████▊| 513/520 [32:06<00:25, 3.62s/it] {'loss': 1.2165, 'grad_norm': 0.0007976299586192372, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [32:06<00:25, 3.62s/it] 99%|█████████▉| 514/520 [32:10<00:21, 3.61s/it] {'loss': 1.1851, 'grad_norm': 0.0006679011041433863, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 99%|█████████▉| 514/520 [32:10<00:21, 3.61s/it] 99%|█████████▉| 515/520 [32:13<00:18, 3.60s/it] {'loss': 1.2333, 'grad_norm': 0.0008494179226821645, 'learning_rate': 4.856389714723575e-05, 'epoch': 0.99} + 99%|█████████▉| 515/520 [32:13<00:18, 3.60s/it] 99%|█████████▉| 516/520 [32:17<00:14, 3.60s/it] {'loss': 1.1476, 'grad_norm': 0.0006956865656936468, 'learning_rate': 3.108179991837545e-05, 'epoch': 0.99} + 99%|█████████▉| 516/520 [32:17<00:14, 3.60s/it] 99%|█████████▉| 517/520 [32:21<00:10, 3.58s/it] {'loss': 1.1646, 'grad_norm': 0.0006721422545235069, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:21<00:10, 3.58s/it] 100%|█████████▉| 518/520 [32:24<00:07, 3.57s/it] {'loss': 1.1533, 'grad_norm': 0.0007497724656278918, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:24<00:07, 3.57s/it] 100%|█████████▉| 519/520 [32:28<00:03, 3.57s/it] {'loss': 1.1391, 'grad_norm': 0.0006807430018069083, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:28<00:03, 3.57s/it] 100%|██████████| 520/520 [32:32<00:00, 3.84s/it] {'loss': 1.1268, 'grad_norm': 0.0006980699221257067, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:32<00:00, 3.84s/it] {'train_runtime': 1952.7368, 'train_samples_per_second': 34.07, 'train_steps_per_second': 0.266, 'train_loss': 1.2159024505661085, 'epoch': 1.0} + 100%|██████████| 520/520 [32:32<00:00, 3.84s/it] 100%|██████████| 520/520 [32:32<00:00, 3.76s/it] +[2025-10-17 23:06:30,258] [INFO] [launch.py:348:main] Process 723259 exits successfully. +[2025-10-17 23:06:30,258] [INFO] [launch.py:348:main] Process 723261 exits successfully. +[2025-10-17 23:06:30,259] [INFO] [launch.py:348:main] Process 723265 exits successfully. +[2025-10-17 23:06:31,260] [INFO] [launch.py:348:main] Process 723264 exits successfully. +[2025-10-17 23:06:31,261] [INFO] [launch.py:348:main] Process 723260 exits successfully. +[2025-10-17 23:06:31,261] [INFO] [launch.py:348:main] Process 723263 exits successfully. +[2025-10-17 23:06:31,262] [INFO] [launch.py:348:main] Process 723262 exits successfully. +[2025-10-17 23:06:35,267] [INFO] [launch.py:348:main] Process 723258 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.3_2e-1_connector-7.0_2.3_2e-1_ablation_20251017_223225.log +Timestamp: 2025-10-17 23:06:37 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation_20251017_230637.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation_20251017_230637.log new file mode 100644 index 0000000000000000000000000000000000000000..e100e363dd5b10a256d418f3aa1905a9fb88e89f --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation_20251017_230637.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation_20251017_230637.log +Timestamp: 2025-10-17 23:06:37 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:06:40,504] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:43,502] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 23:06:43,504] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 2.5 --temperature_mlp_text 2.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 2.5 --temperature_mlp_vision 2.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 2.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:06:46,064] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:47,119] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 23:06:47,119] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 23:06:47,119] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 23:06:47,119] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 23:06:47,119] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 23:06:47,119] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 23:06:47,119] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 23:06:47,121] [INFO] [launch.py:253:main] process 745084 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,123] [INFO] [launch.py:253:main] process 745085 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,125] [INFO] [launch.py:253:main] process 745086 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,127] [INFO] [launch.py:253:main] process 745087 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,129] [INFO] [launch.py:253:main] process 745088 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,131] [INFO] [launch.py:253:main] process 745089 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,133] [INFO] [launch.py:253:main] process 745090 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:06:47,135] [INFO] [launch.py:253:main] process 745091 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:06:53,870] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,107] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,203] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,236] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,255] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,263] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,295] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,301] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,316] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:06:54,513] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,602] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,635] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,652] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,657] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,701] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:06:54,701] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 23:06:54,707] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.5, 'temperature_mlp': 2.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.5, + "temperature_mlp": 2.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:745084:745084 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:745085:745085 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:745090:745090 [6] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:745087:745087 [3] NCCL INFO NET/Plugin: Using internal network plugin. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Connected all rings 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15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745091:746715 [7] NCCL INFO ncclCommInitRank comm 0x561a9da579c0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745089:746717 [5] NCCL INFO ncclCommInitRank comm 0x55b17f708d00 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745088:746716 [4] NCCL INFO ncclCommInitRank comm 0x55705a913320 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745086:746751 [2] NCCL INFO ncclCommInitRank comm 0x561086673900 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745085:746712 [1] NCCL INFO ncclCommInitRank comm 0x55a769b52f00 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745087:746714 [3] NCCL INFO ncclCommInitRank comm 0x5566eb633c60 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745084:746711 [0] NCCL INFO ncclCommInitRank comm 0x5622c47b8ac0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x76850dbd592c55c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:745090:746713 [6] NCCL INFO ncclCommInitRank comm 0x55e5fe8b9000 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x76850dbd592c55c6 - Init COMPLETE +[2025-10-17 23:07:36,248] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model + +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 23:07:37,962] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 23:07:56,289 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 23:07:56,294 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:002->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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Connected all trees +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745085:751650 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745084:751646 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745086:751652 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745087:751648 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745088:751649 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:745089:751651 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:745091:751653 [7] NCCL INFO ncclCommInitRank comm 0x7fe43006a4d0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x838671a4937a93fc - Init COMPLETE +ywang29-vrdb-test1-worker-0:745090:751647 [6] NCCL INFO ncclCommInitRank comm 0x7ef99c06b070 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x838671a4937a93fc - Init COMPLETE 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0x7fc0a406b090 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x838671a4937a93fc - Init COMPLETE + 0%| | 1/520 [00:17<2:30:48, 17.44s/it] {'loss': 2.1026, 'grad_norm': 0.013044066536449546, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:17<2:30:48, 17.44s/it] 0%| | 2/520 [00:21<1:21:10, 9.40s/it] {'loss': 2.099, 'grad_norm': 0.014033251790540616, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:21<1:21:10, 9.40s/it] 1%| | 3/520 [00:25<59:11, 6.87s/it] {'loss': 2.2481, 'grad_norm': 0.01604808137972134, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:25<59:11, 6.87s/it] 1%| | 4/520 [00:28<48:45, 5.67s/it] {'loss': 1.6958, 'grad_norm': 0.0038691363320910335, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:28<48:45, 5.67s/it] 1%| | 5/520 [00:32<42:56, 5.00s/it] {'loss': 1.7033, 'grad_norm': 0.004227101033435108, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:32<42:56, 5.00s/it] 1%| | 6/520 [00:36<39:29, 4.61s/it] {'loss': 1.4132, 'grad_norm': 0.0017935335807305601, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:36<39:29, 4.61s/it] 1%|▏ | 7/520 [00:40<37:11, 4.35s/it] {'loss': 1.4483, 'grad_norm': 0.0024232239740940116, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:40<37:11, 4.35s/it] 2%|▏ | 8/520 [00:44<37:36, 4.41s/it] {'loss': 1.4884, 'grad_norm': 0.0023879937791677296, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:44<37:36, 4.41s/it] 2%|▏ | 9/520 [00:49<37:37, 4.42s/it] {'loss': 1.5544, 'grad_norm': 0.0020358859074236678, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:49<37:37, 4.42s/it] 2%|▏ | 10/520 [00:53<36:08, 4.25s/it] {'loss': 1.3787, 'grad_norm': 0.0015950986604093174, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:53<36:08, 4.25s/it] 2%|▏ | 11/520 [00:57<35:20, 4.17s/it] {'loss': 1.4423, 'grad_norm': 0.001363476890853184, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:57<35:20, 4.17s/it] 2%|▏ | 12/520 [01:01<34:31, 4.08s/it] {'loss': 1.3433, 'grad_norm': 0.0014670064553374293, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:01<34:31, 4.08s/it][2025-10-17 23:09:06,210] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:05<35:33, 4.21s/it] {'loss': 1.3814, 'grad_norm': 0.0015846120295597045, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:05<35:33, 4.21s/it] 3%|▎ | 14/520 [01:09<34:36, 4.10s/it] {'loss': 1.4346, 'grad_norm': 0.0018185204912648943, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:09<34:36, 4.10s/it] 3%|▎ | 15/520 [01:13<33:54, 4.03s/it] {'loss': 1.3858, 'grad_norm': 0.0011323346257682949, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:13<33:54, 4.03s/it] 3%|▎ | 16/520 [01:17<33:24, 3.98s/it] {'loss': 1.339, 'grad_norm': 0.001158678618394903, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:17<33:24, 3.98s/it] 3%|▎ | 17/520 [01:21<33:02, 3.94s/it] {'loss': 1.4522, 'grad_norm': 0.0013150950193712558, 'learning_rate': 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'learning_rate': 0.08756562953525152, 'epoch': 0.55} + 55%|█████▌ | 288/520 [18:03<14:09, 3.66s/it] 56%|█████▌ | 289/520 [18:07<14:06, 3.67s/it] {'loss': 1.1848, 'grad_norm': 0.0007578370102373155, 'learning_rate': 0.08694738077799487, 'epoch': 0.56} + 56%|█████▌ | 289/520 [18:07<14:06, 3.67s/it] 56%|█████▌ | 290/520 [18:11<14:01, 3.66s/it] {'loss': 1.1145, 'grad_norm': 0.0007478316998492703, 'learning_rate': 0.08632963916899268, 'epoch': 0.56} + 56%|█████▌ | 290/520 [18:11<14:01, 3.66s/it] 56%|█████▌ | 291/520 [18:14<14:02, 3.68s/it] {'loss': 1.1543, 'grad_norm': 0.0007567856574146194, 'learning_rate': 0.08571242871006202, 'epoch': 0.56} + 56%|█████▌ | 291/520 [18:14<14:02, 3.68s/it] 56%|█████▌ | 292/520 [18:18<13:56, 3.67s/it] {'loss': 1.2054, 'grad_norm': 0.0007700996803588552, 'learning_rate': 0.08509577338238256, 'epoch': 0.56} + 56%|█████▌ | 292/520 [18:18<13:56, 3.67s/it] 56%|█████▋ | 293/520 [18:22<13:51, 3.66s/it] {'loss': 1.1588, 'grad_norm': 0.0008289452501274292, 'learning_rate': 0.08447969714556484, 'epoch': 0.56} + 56%|█████▋ | 293/520 [18:22<13:51, 3.66s/it] 57%|█████▋ | 294/520 [18:25<13:47, 3.66s/it] {'loss': 1.1766, 'grad_norm': 0.0008631703903787514, 'learning_rate': 0.08386422393671933, 'epoch': 0.57} + 57%|█████▋ | 294/520 [18:25<13:47, 3.66s/it] 57%|█████▋ | 295/520 [18:29<13:43, 3.66s/it] {'loss': 1.1761, 'grad_norm': 0.000862471073487921, 'learning_rate': 0.08324937766952638, 'epoch': 0.57} + 57%|█████▋ | 295/520 [18:29<13:43, 3.66s/it] 57%|█████▋ | 296/520 [18:33<13:41, 3.67s/it] {'loss': 1.127, 'grad_norm': 0.0008435767206833445, 'learning_rate': 0.08263518223330697, 'epoch': 0.57} + 57%|█████▋ | 296/520 [18:33<13:41, 3.67s/it] 57%|█████▋ | 297/520 [18:36<13:37, 3.67s/it] {'loss': 1.2584, 'grad_norm': 0.0008872874743044388, 'learning_rate': 0.08202166149209474, 'epoch': 0.57} + 57%|█████▋ | 297/520 [18:36<13:37, 3.67s/it] 57%|█████▋ | 298/520 [18:40<13:33, 3.66s/it] {'loss': 1.2187, 'grad_norm': 0.0007860505528460949, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:40<13:33, 3.66s/it] 57%|█████▊ | 299/520 [18:44<13:29, 3.66s/it] {'loss': 1.2211, 'grad_norm': 0.0007322676821547985, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:44<13:29, 3.66s/it] 58%|█████▊ | 300/520 [18:47<13:24, 3.66s/it] {'loss': 1.2646, 'grad_norm': 0.0007965194739241169, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:47<13:24, 3.66s/it] 58%|█████▊ | 301/520 [18:51<13:19, 3.65s/it] {'loss': 1.2496, 'grad_norm': 0.0007644009723686502, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:51<13:19, 3.65s/it] 58%|█████▊ | 302/520 [18:55<13:16, 3.65s/it] {'loss': 1.2285, 'grad_norm': 0.00086977282696785, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:55<13:16, 3.65s/it] 58%|█████▊ | 303/520 [18:58<13:12, 3.65s/it] {'loss': 1.1742, 'grad_norm': 0.0009096427630679745, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:58<13:12, 3.65s/it] 58%|█████▊ | 304/520 [19:02<13:12, 3.67s/it] {'loss': 1.1407, 'grad_norm': 0.0008700991069732981, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [19:02<13:12, 3.67s/it] 59%|█████▊ | 305/520 [19:06<13:06, 3.66s/it] {'loss': 1.2758, 'grad_norm': 0.0009217929120065603, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:06<13:06, 3.66s/it] 59%|█████▉ | 306/520 [19:09<13:03, 3.66s/it] {'loss': 1.2226, 'grad_norm': 0.0008574728105391464, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:09<13:03, 3.66s/it] 59%|█████▉ | 307/520 [19:13<13:30, 3.81s/it] {'loss': 1.1653, 'grad_norm': 0.0008020581764277724, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:13<13:30, 3.81s/it] 59%|█████▉ | 308/520 [19:17<13:18, 3.76s/it] {'loss': 1.2767, 'grad_norm': 0.0008478442384767844, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:17<13:18, 3.76s/it] 59%|█████▉ | 309/520 [19:21<13:06, 3.73s/it] {'loss': 1.1675, 'grad_norm': 0.0007608280110964724, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:21<13:06, 3.73s/it] 60%|█████▉ | 310/520 [19:24<12:58, 3.71s/it] {'loss': 1.1491, 'grad_norm': 0.0007916255001501632, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:24<12:58, 3.71s/it] 60%|█████▉ | 311/520 [19:28<12:53, 3.70s/it] {'loss': 1.1228, 'grad_norm': 0.0008032560024184856, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:28<12:53, 3.70s/it] 60%|██████ | 312/520 [19:32<12:46, 3.69s/it] {'loss': 1.1165, 'grad_norm': 0.0008249768302273488, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:32<12:46, 3.69s/it] 60%|██████ | 313/520 [19:36<12:54, 3.74s/it] {'loss': 1.1021, 'grad_norm': 0.0007232234454638091, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:36<12:54, 3.74s/it] 60%|██████ | 314/520 [19:40<13:28, 3.92s/it] {'loss': 1.1373, 'grad_norm': 0.0007546417912541701, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:40<13:28, 3.92s/it] 61%|██████ | 315/520 [19:44<13:20, 3.90s/it] {'loss': 1.1855, 'grad_norm': 0.0010057240342216446, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:44<13:20, 3.90s/it] 61%|██████ | 316/520 [19:48<13:40, 4.02s/it] {'loss': 1.1221, 'grad_norm': 0.0008490373484921477, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:48<13:40, 4.02s/it] 61%|██████ | 317/520 [19:52<13:50, 4.09s/it] {'loss': 1.1295, 'grad_norm': 0.0006958556928239139, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:52<13:50, 4.09s/it] 61%|██████ | 318/520 [19:56<13:32, 4.02s/it] {'loss': 1.2389, 'grad_norm': 0.0008550445417346832, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:56<13:32, 4.02s/it] 61%|██████▏ | 319/520 [20:01<13:41, 4.09s/it] {'loss': 1.1188, 'grad_norm': 0.0007145459013494734, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [20:01<13:41, 4.09s/it] 62%|██████▏ | 320/520 [20:04<13:23, 4.02s/it] {'loss': 1.0679, 'grad_norm': 0.0008148985688132301, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:04<13:23, 4.02s/it] 62%|██████▏ | 321/520 [20:08<13:10, 3.97s/it] {'loss': 1.2597, 'grad_norm': 0.0008272942557871211, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:08<13:10, 3.97s/it] 62%|██████▏ | 322/520 [20:12<13:00, 3.94s/it] {'loss': 1.0883, 'grad_norm': 0.000729821620558341, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:12<13:00, 3.94s/it] 62%|██████▏ | 323/520 [20:16<12:50, 3.91s/it] {'loss': 1.1571, 'grad_norm': 0.0007647113846127747, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:16<12:50, 3.91s/it] 62%|██████▏ | 324/520 [20:20<12:41, 3.88s/it] {'loss': 1.2026, 'grad_norm': 0.0008304482350024525, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:20<12:41, 3.88s/it] 62%|██████▎ | 325/520 [20:23<12:27, 3.83s/it] {'loss': 1.1983, 'grad_norm': 0.0008310034067486729, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:23<12:27, 3.83s/it] 63%|██████▎ | 326/520 [20:27<12:12, 3.77s/it] {'loss': 1.1993, 'grad_norm': 0.0008149794891161367, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:27<12:12, 3.77s/it] 63%|██████▎ | 327/520 [20:31<12:03, 3.75s/it] {'loss': 1.1883, 'grad_norm': 0.0007925089788650172, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:31<12:03, 3.75s/it] 63%|██████▎ | 328/520 [20:34<11:53, 3.72s/it] {'loss': 1.2415, 'grad_norm': 0.0008782383510696865, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:34<11:53, 3.72s/it] 63%|██████▎ | 329/520 [20:38<11:46, 3.70s/it] {'loss': 1.1257, 'grad_norm': 0.0006946990578085948, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:38<11:46, 3.70s/it] 63%|██████▎ | 330/520 [20:42<11:40, 3.68s/it] {'loss': 1.195, 'grad_norm': 0.0007392114312790961, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:42<11:40, 3.68s/it] 64%|██████▎ | 331/520 [20:45<11:34, 3.67s/it] {'loss': 1.1582, 'grad_norm': 0.0007788163032131735, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:45<11:34, 3.67s/it] 64%|██████▍ | 332/520 [20:49<11:30, 3.67s/it] {'loss': 1.2176, 'grad_norm': 0.00070014806788337, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:49<11:30, 3.67s/it] 64%|██████▍ | 333/520 [20:53<11:26, 3.67s/it] {'loss': 1.2914, 'grad_norm': 0.0008335744079543267, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:53<11:26, 3.67s/it] 64%|██████▍ | 334/520 [20:56<11:22, 3.67s/it] {'loss': 1.2038, 'grad_norm': 0.000813917936034947, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:56<11:22, 3.67s/it] 64%|██████▍ | 335/520 [21:00<11:19, 3.67s/it] {'loss': 1.2045, 'grad_norm': 0.000731044642035904, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:00<11:19, 3.67s/it] 65%|██████▍ | 336/520 [21:04<11:17, 3.68s/it] {'loss': 1.1083, 'grad_norm': 0.0008460543541565234, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:04<11:17, 3.68s/it] 65%|██████▍ | 337/520 [21:07<11:14, 3.69s/it] {'loss': 1.099, 'grad_norm': 0.0007810868523413174, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:07<11:14, 3.69s/it] 65%|██████▌ | 338/520 [21:11<11:12, 3.69s/it] {'loss': 1.205, 'grad_norm': 0.0007735519651260445, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:11<11:12, 3.69s/it] 65%|██████▌ | 339/520 [21:15<11:07, 3.69s/it] {'loss': 1.1524, 'grad_norm': 0.0007794159712442512, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:15<11:07, 3.69s/it] 65%|██████▌ | 340/520 [21:19<11:05, 3.70s/it] {'loss': 1.1441, 'grad_norm': 0.0007856404300666732, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:19<11:05, 3.70s/it] 66%|██████▌ | 341/520 [21:22<11:01, 3.69s/it] {'loss': 1.1685, 'grad_norm': 0.000816388523167404, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:22<11:01, 3.69s/it] 66%|██████▌ | 342/520 [21:26<10:54, 3.68s/it] {'loss': 1.1919, 'grad_norm': 0.000888497570668152, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:26<10:54, 3.68s/it] 66%|██████▌ | 343/520 [21:30<10:51, 3.68s/it] {'loss': 1.1427, 'grad_norm': 0.0007728798619441229, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:30<10:51, 3.68s/it] 66%|██████▌ | 344/520 [21:33<10:47, 3.68s/it] {'loss': 1.1256, 'grad_norm': 0.0007541522495959705, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:33<10:47, 3.68s/it] 66%|██████▋ | 345/520 [21:37<10:44, 3.68s/it] {'loss': 1.2274, 'grad_norm': 0.000813041910417102, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:37<10:44, 3.68s/it] 67%|██████▋ | 346/520 [21:41<10:49, 3.73s/it] {'loss': 1.166, 'grad_norm': 0.0008473836799399349, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:41<10:49, 3.73s/it] 67%|██████▋ | 347/520 [21:45<10:48, 3.75s/it] {'loss': 1.1404, 'grad_norm': 0.0007293202817686503, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:45<10:48, 3.75s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:48<10:49, 3.78s/it] {'loss': 1.0985, 'grad_norm': 0.0009870354724659293, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:48<10:49, 3.78s/it] 67%|██████▋ | 349/520 [21:52<10:47, 3.79s/it] {'loss': 1.1364, 'grad_norm': 0.0007643048753767549, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:52<10:47, 3.79s/it] 67%|██████▋ | 350/520 [21:56<10:35, 3.74s/it] {'loss': 1.1793, 'grad_norm': 0.0008920277008605555, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:56<10:35, 3.74s/it] 68%|██████▊ | 351/520 [22:00<10:27, 3.71s/it] {'loss': 1.0893, 'grad_norm': 0.0007301501453548688, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:00<10:27, 3.71s/it] 68%|██████▊ | 352/520 [22:03<10:20, 3.69s/it] {'loss': 1.2069, 'grad_norm': 0.0007484678822521191, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:03<10:20, 3.69s/it] 68%|██████▊ | 353/520 [22:07<10:15, 3.68s/it] {'loss': 1.1315, 'grad_norm': 0.00071717989013489, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:07<10:15, 3.68s/it] 68%|██████▊ | 354/520 [22:10<10:09, 3.67s/it] {'loss': 1.2284, 'grad_norm': 0.0007135680985632659, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:10<10:09, 3.67s/it] 68%|██████▊ | 355/520 [22:14<10:07, 3.68s/it] {'loss': 1.1511, 'grad_norm': 0.0007724517810903692, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:14<10:07, 3.68s/it] 68%|██████▊ | 356/520 [22:18<10:10, 3.72s/it] {'loss': 1.1527, 'grad_norm': 0.0007955112054354335, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:18<10:10, 3.72s/it] 69%|██████▊ | 357/520 [22:22<10:10, 3.75s/it] {'loss': 1.183, 'grad_norm': 0.0007400150857230014, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:22<10:10, 3.75s/it] 69%|██████▉ | 358/520 [22:26<10:04, 3.73s/it] {'loss': 1.1158, 'grad_norm': 0.0007566426585063794, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:26<10:04, 3.73s/it] 69%|██████▉ | 359/520 [22:29<09:57, 3.71s/it] {'loss': 1.1687, 'grad_norm': 0.000871204552778568, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:29<09:57, 3.71s/it] 69%|██████▉ | 360/520 [22:33<09:51, 3.69s/it] {'loss': 1.1775, 'grad_norm': 0.0008479672545832928, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:33<09:51, 3.69s/it] 69%|██████▉ | 361/520 [22:36<09:45, 3.69s/it] {'loss': 1.1904, 'grad_norm': 0.000706805180330504, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:36<09:45, 3.69s/it] 70%|██████▉ | 362/520 [22:40<09:40, 3.68s/it] {'loss': 1.1649, 'grad_norm': 0.0008291905948066463, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:40<09:40, 3.68s/it] 70%|██████▉ | 363/520 [22:44<09:37, 3.68s/it] {'loss': 1.1893, 'grad_norm': 0.0007661150870576956, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:44<09:37, 3.68s/it] 70%|███████ | 364/520 [22:48<09:34, 3.68s/it] {'loss': 1.2081, 'grad_norm': 0.0007613883456829563, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:48<09:34, 3.68s/it] 70%|███████ | 365/520 [22:51<09:29, 3.67s/it] {'loss': 1.2438, 'grad_norm': 0.0008083817321654701, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:51<09:29, 3.67s/it] 70%|███████ | 366/520 [22:55<09:25, 3.67s/it] {'loss': 1.2053, 'grad_norm': 0.0007457256896135567, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:55<09:25, 3.67s/it] 71%|███████ | 367/520 [22:59<09:21, 3.67s/it] {'loss': 1.2069, 'grad_norm': 0.0007835876611965302, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:59<09:21, 3.67s/it] 71%|███████ | 368/520 [23:02<09:17, 3.67s/it] {'loss': 1.0605, 'grad_norm': 0.0007875598227272411, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:02<09:17, 3.67s/it] 71%|███████ | 369/520 [23:06<09:13, 3.67s/it] {'loss': 1.1653, 'grad_norm': 0.000702148470316767, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:06<09:13, 3.67s/it] 71%|███████ | 370/520 [23:09<09:09, 3.66s/it] {'loss': 1.1222, 'grad_norm': 0.0007330240876463761, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:09<09:09, 3.66s/it] 71%|███████▏ | 371/520 [23:13<09:06, 3.67s/it] {'loss': 1.118, 'grad_norm': 0.000818798088919384, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:13<09:06, 3.67s/it] 72%|███████▏ | 372/520 [23:17<09:01, 3.66s/it] {'loss': 1.2349, 'grad_norm': 0.000782973205674251, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:17<09:01, 3.66s/it] 72%|███████▏ | 373/520 [23:20<08:57, 3.66s/it] {'loss': 1.1257, 'grad_norm': 0.0008031886348622071, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:20<08:57, 3.66s/it] 72%|███████▏ | 374/520 [23:24<08:52, 3.65s/it] {'loss': 1.2065, 'grad_norm': 0.0007813423128572903, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:24<08:52, 3.65s/it] 72%|███████▏ | 375/520 [23:28<08:48, 3.64s/it] {'loss': 1.1258, 'grad_norm': 0.0008021193883624428, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:28<08:48, 3.64s/it] 72%|███████▏ | 376/520 [23:31<08:44, 3.64s/it] {'loss': 1.2316, 'grad_norm': 0.0007432436883965108, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:31<08:44, 3.64s/it] 72%|███████▎ | 377/520 [23:35<08:40, 3.64s/it] {'loss': 1.1622, 'grad_norm': 0.0007792001110817601, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:35<08:40, 3.64s/it] 73%|███████▎ | 378/520 [23:39<08:36, 3.64s/it] {'loss': 1.2264, 'grad_norm': 0.0007458044535070406, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:39<08:36, 3.64s/it] 73%|███████▎ | 379/520 [23:42<08:32, 3.64s/it] {'loss': 1.199, 'grad_norm': 0.0007561785010094922, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:42<08:32, 3.64s/it] 73%|███████▎ | 380/520 [23:46<08:29, 3.64s/it] {'loss': 1.2119, 'grad_norm': 0.0007610861273052629, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:46<08:29, 3.64s/it] 73%|███████▎ | 381/520 [23:50<08:27, 3.65s/it] {'loss': 1.2032, 'grad_norm': 0.0007396154319207574, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:50<08:27, 3.65s/it] 73%|███████▎ | 382/520 [23:53<08:24, 3.66s/it] {'loss': 1.1825, 'grad_norm': 0.0007259485925482741, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:53<08:24, 3.66s/it] 74%|███████▎ | 383/520 [23:57<08:20, 3.65s/it] {'loss': 1.0445, 'grad_norm': 0.0008682905389777291, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:57<08:20, 3.65s/it] 74%|███████▍ | 384/520 [24:01<08:15, 3.65s/it] {'loss': 1.2113, 'grad_norm': 0.0007199138843452742, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:01<08:15, 3.65s/it] 74%|███████▍ | 385/520 [24:04<08:17, 3.68s/it] {'loss': 1.1841, 'grad_norm': 0.0007119258595516435, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:04<08:17, 3.68s/it] 74%|███████▍ | 386/520 [24:08<08:19, 3.73s/it] {'loss': 1.1393, 'grad_norm': 0.0006769030997858328, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:08<08:19, 3.73s/it] 74%|███████▍ | 387/520 [24:12<08:14, 3.71s/it] {'loss': 1.234, 'grad_norm': 0.000797551670104832, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:12<08:14, 3.71s/it] 75%|███████▍ | 388/520 [24:15<08:08, 3.70s/it] {'loss': 1.0954, 'grad_norm': 0.0007441757921838072, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:15<08:08, 3.70s/it] 75%|███████▍ | 389/520 [24:19<08:05, 3.71s/it] {'loss': 1.1408, 'grad_norm': 0.0008985028038829149, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:19<08:05, 3.71s/it] 75%|███████▌ | 390/520 [24:23<08:00, 3.70s/it] {'loss': 1.2057, 'grad_norm': 0.0007541498658404436, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:23<08:00, 3.70s/it] 75%|███████▌ | 391/520 [24:27<07:59, 3.72s/it] {'loss': 1.2746, 'grad_norm': 0.0008205087243499658, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:27<07:59, 3.72s/it] 75%|███████▌ | 392/520 [24:30<07:57, 3.73s/it] {'loss': 1.0983, 'grad_norm': 0.0008586679529577998, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:30<07:57, 3.73s/it] 76%|███████▌ | 393/520 [24:34<07:53, 3.73s/it] {'loss': 1.0895, 'grad_norm': 0.0006392045314624493, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:34<07:53, 3.73s/it] 76%|███████▌ | 394/520 [24:38<07:46, 3.70s/it] {'loss': 1.1643, 'grad_norm': 0.0008248783268678953, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:38<07:46, 3.70s/it] 76%|███████▌ | 395/520 [24:41<07:42, 3.70s/it] {'loss': 1.1286, 'grad_norm': 0.0008923881543445726, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:41<07:42, 3.70s/it] 76%|███████▌ | 396/520 [24:45<07:36, 3.68s/it] {'loss': 1.2077, 'grad_norm': 0.0008224088659312242, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:45<07:36, 3.68s/it] 76%|███████▋ | 397/520 [24:49<07:32, 3.68s/it] {'loss': 1.1829, 'grad_norm': 0.0007253998093127268, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:49<07:32, 3.68s/it] 77%|███████▋ | 398/520 [24:52<07:29, 3.68s/it] {'loss': 1.1837, 'grad_norm': 0.0008067587192170749, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:52<07:29, 3.68s/it] 77%|███████▋ | 399/520 [24:56<07:26, 3.69s/it] {'loss': 1.1257, 'grad_norm': 0.000721053507490859, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:56<07:26, 3.69s/it] 77%|███████▋ | 400/520 [25:00<07:23, 3.70s/it] {'loss': 1.1567, 'grad_norm': 0.0007315628130436664, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:00<07:23, 3.70s/it] 77%|███████▋ | 401/520 [25:04<07:22, 3.72s/it] {'loss': 1.0207, 'grad_norm': 0.0008299093910447188, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:04<07:22, 3.72s/it] 77%|███████▋ | 402/520 [25:07<07:18, 3.71s/it] {'loss': 1.1471, 'grad_norm': 0.000795053849776354, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:07<07:18, 3.71s/it] 78%|███████▊ | 403/520 [25:11<07:12, 3.70s/it] {'loss': 1.1691, 'grad_norm': 0.0008456393595949487, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:11<07:12, 3.70s/it] 78%|███████▊ | 404/520 [25:15<07:06, 3.67s/it] {'loss': 1.08, 'grad_norm': 0.0008846102874051853, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:15<07:06, 3.67s/it] 78%|███████▊ | 405/520 [25:18<07:00, 3.66s/it] {'loss': 1.1398, 'grad_norm': 0.0008661573938648867, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:18<07:00, 3.66s/it] 78%|███████▊ | 406/520 [25:22<06:57, 3.67s/it] {'loss': 1.0556, 'grad_norm': 0.0010709557857332941, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:22<06:57, 3.67s/it] 78%|███████▊ | 407/520 [25:26<06:54, 3.67s/it] {'loss': 1.2457, 'grad_norm': 0.0008552664238172506, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:26<06:54, 3.67s/it] 78%|███████▊ | 408/520 [25:29<06:49, 3.65s/it] {'loss': 1.1618, 'grad_norm': 0.0009724930307077197, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:29<06:49, 3.65s/it] 79%|███████▊ | 409/520 [25:33<06:44, 3.65s/it] {'loss': 1.2724, 'grad_norm': 0.0008270334604449582, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:33<06:44, 3.65s/it] 79%|███████▉ | 410/520 [25:36<06:39, 3.63s/it] {'loss': 1.0171, 'grad_norm': 0.0008004055362002128, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:36<06:39, 3.63s/it] 79%|███████▉ | 411/520 [25:40<06:35, 3.63s/it] {'loss': 1.2562, 'grad_norm': 0.0008519648926856964, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:40<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:44<06:31, 3.63s/it] {'loss': 1.1653, 'grad_norm': 0.0007803345364315306, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:44<06:31, 3.63s/it] 79%|███████▉ | 413/520 [25:47<06:28, 3.63s/it] {'loss': 1.1487, 'grad_norm': 0.0007195399699104335, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:47<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:51<06:24, 3.63s/it] {'loss': 0.9654, 'grad_norm': 0.0006543174830557237, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:51<06:24, 3.63s/it] 80%|███████▉ | 415/520 [25:55<06:22, 3.64s/it] {'loss': 1.1484, 'grad_norm': 0.0007325471958152538, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:55<06:22, 3.64s/it] 80%|████████ | 416/520 [25:58<06:17, 3.63s/it] {'loss': 1.0587, 'grad_norm': 0.0008261675427264581, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:58<06:17, 3.63s/it] 80%|████████ | 417/520 [26:02<06:16, 3.66s/it] {'loss': 1.218, 'grad_norm': 0.0008071379443057968, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:02<06:16, 3.66s/it] 80%|████████ | 418/520 [26:06<06:11, 3.64s/it] {'loss': 1.2094, 'grad_norm': 0.0007796009600641559, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:06<06:11, 3.64s/it] 81%|████████ | 419/520 [26:09<06:08, 3.64s/it] {'loss': 1.2026, 'grad_norm': 0.0008245777382180091, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:09<06:08, 3.64s/it] 81%|████████ | 420/520 [26:13<06:04, 3.64s/it] {'loss': 1.0981, 'grad_norm': 0.0008213869431179964, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:13<06:04, 3.64s/it] 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] {'loss': 1.0326, 'grad_norm': 0.0008821664843703975, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] 81%|████████ | 422/520 [26:20<05:56, 3.63s/it] {'loss': 1.1517, 'grad_norm': 0.0008005033913973539, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:20<05:56, 3.63s/it] 81%|████████▏ | 423/520 [26:24<05:52, 3.64s/it] {'loss': 1.1256, 'grad_norm': 0.0008338161222117834, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:24<05:52, 3.64s/it] 82%|████████▏ | 424/520 [26:27<05:49, 3.64s/it] {'loss': 1.2343, 'grad_norm': 0.0007487046676465901, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:27<05:49, 3.64s/it] 82%|████████▏ | 425/520 [26:31<05:45, 3.63s/it] {'loss': 1.1428, 'grad_norm': 0.0007406614034705989, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:31<05:45, 3.63s/it] 82%|████████▏ | 426/520 [26:35<05:41, 3.63s/it] {'loss': 1.1649, 'grad_norm': 0.0009628083463473568, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:35<05:41, 3.63s/it] 82%|████████▏ | 427/520 [26:38<05:36, 3.62s/it] {'loss': 1.0753, 'grad_norm': 0.000742980325910809, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:38<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:42<05:33, 3.63s/it] {'loss': 1.0625, 'grad_norm': 0.00081874436097046, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:42<05:33, 3.63s/it] 82%|████████▎ | 429/520 [26:46<05:31, 3.64s/it] {'loss': 1.1565, 'grad_norm': 0.0008241321836533435, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:46<05:31, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:49<05:27, 3.64s/it] {'loss': 1.1578, 'grad_norm': 0.0007146138826057369, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:49<05:27, 3.64s/it] 83%|████████▎ | 431/520 [26:53<05:24, 3.64s/it] {'loss': 1.1226, 'grad_norm': 0.0008473010238568084, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:53<05:24, 3.64s/it] 83%|████████▎ | 432/520 [26:56<05:20, 3.64s/it] {'loss': 1.069, 'grad_norm': 0.0007684923385758307, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:56<05:20, 3.64s/it] 83%|████████▎ | 433/520 [27:00<05:16, 3.64s/it] {'loss': 1.1996, 'grad_norm': 0.0008022370423788713, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:00<05:16, 3.64s/it] 83%|████████▎ | 434/520 [27:04<05:18, 3.70s/it] {'loss': 0.9502, 'grad_norm': 0.0007652597466712863, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:04<05:18, 3.70s/it] 84%|████████▎ | 435/520 [27:08<05:16, 3.72s/it] {'loss': 1.229, 'grad_norm': 0.0008616877283127614, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:08<05:16, 3.72s/it] 84%|████████▍ | 436/520 [27:12<05:14, 3.74s/it] {'loss': 1.0386, 'grad_norm': 0.0007837032687431649, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:12<05:14, 3.74s/it] 84%|████████▍ | 437/520 [27:15<05:08, 3.72s/it] {'loss': 1.2524, 'grad_norm': 0.0007871956155819779, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:15<05:08, 3.72s/it] 84%|████████▍ | 438/520 [27:19<05:03, 3.70s/it] {'loss': 1.0782, 'grad_norm': 0.0007918587076953434, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:19<05:03, 3.70s/it] 84%|████████▍ | 439/520 [27:23<04:58, 3.69s/it] {'loss': 1.1068, 'grad_norm': 0.000635847916463321, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:23<04:58, 3.69s/it] 85%|████████▍ | 440/520 [27:26<04:53, 3.67s/it] {'loss': 1.1068, 'grad_norm': 0.0007826164824137219, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:26<04:53, 3.67s/it] 85%|████████▍ | 441/520 [27:30<04:49, 3.67s/it] {'loss': 1.119, 'grad_norm': 0.000802215422418088, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:30<04:49, 3.67s/it] 85%|████████▌ | 442/520 [27:33<04:45, 3.66s/it] {'loss': 1.173, 'grad_norm': 0.0008479482094829124, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:33<04:45, 3.66s/it] 85%|████████▌ | 443/520 [27:37<04:41, 3.65s/it] {'loss': 1.186, 'grad_norm': 0.0007547757136845783, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:37<04:41, 3.65s/it] 85%|████████▌ | 444/520 [27:41<04:37, 3.66s/it] {'loss': 1.1499, 'grad_norm': 0.0006933206219245107, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:41<04:37, 3.66s/it] 86%|████████▌ | 445/520 [27:44<04:34, 3.66s/it] {'loss': 1.078, 'grad_norm': 0.0007779206738579252, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:44<04:34, 3.66s/it] 86%|████████▌ | 446/520 [27:48<04:30, 3.66s/it] {'loss': 1.1957, 'grad_norm': 0.0007083548988854813, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:48<04:30, 3.66s/it] 86%|████████▌ | 447/520 [27:52<04:27, 3.67s/it] {'loss': 1.1509, 'grad_norm': 0.000745146471046045, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:52<04:27, 3.67s/it] 86%|████████▌ | 448/520 [27:55<04:24, 3.68s/it] {'loss': 1.148, 'grad_norm': 0.0007915630228652564, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:55<04:24, 3.68s/it] 86%|████████▋ | 449/520 [27:59<04:20, 3.66s/it] {'loss': 1.1549, 'grad_norm': 0.0007735150794274497, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:59<04:20, 3.66s/it] 87%|████████▋ | 450/520 [28:03<04:15, 3.65s/it] {'loss': 1.1737, 'grad_norm': 0.000763884889811303, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:03<04:15, 3.65s/it] 87%|████████▋ | 451/520 [28:06<04:12, 3.65s/it] {'loss': 1.1748, 'grad_norm': 0.0007851835594306233, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:06<04:12, 3.65s/it] 87%|████████▋ | 452/520 [28:10<04:07, 3.65s/it] {'loss': 1.199, 'grad_norm': 0.0007120426747298738, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:10<04:07, 3.65s/it] 87%|████████▋ | 453/520 [28:14<04:03, 3.64s/it] {'loss': 1.1753, 'grad_norm': 0.0007362158342215738, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:14<04:03, 3.64s/it] 87%|████████▋ | 454/520 [28:17<03:59, 3.63s/it] {'loss': 1.0848, 'grad_norm': 0.0007807765335241522, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:17<03:59, 3.63s/it] 88%|████████▊ | 455/520 [28:21<03:55, 3.63s/it] {'loss': 1.2253, 'grad_norm': 0.0007692537927567096, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:21<03:55, 3.63s/it] 88%|████████▊ | 456/520 [28:25<03:52, 3.64s/it] {'loss': 1.1539, 'grad_norm': 0.0007769765947825498, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:25<03:52, 3.64s/it] 88%|████████▊ | 457/520 [28:28<03:48, 3.63s/it] {'loss': 1.072, 'grad_norm': 0.000661441196098956, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:28<03:48, 3.63s/it] 88%|████████▊ | 458/520 [28:32<03:45, 3.64s/it] {'loss': 1.2743, 'grad_norm': 0.0008603268163371352, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:32<03:45, 3.64s/it] 88%|████████▊ | 459/520 [28:35<03:43, 3.66s/it] {'loss': 1.2078, 'grad_norm': 0.0008513720787277193, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:35<03:43, 3.66s/it] 88%|████████▊ | 460/520 [28:39<03:38, 3.65s/it] {'loss': 1.1008, 'grad_norm': 0.0007582202133174511, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:39<03:38, 3.65s/it] 89%|████████▊ | 461/520 [28:43<03:35, 3.66s/it] {'loss': 1.1515, 'grad_norm': 0.0006055748385567031, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:43<03:35, 3.66s/it] 89%|████████▉ | 462/520 [28:46<03:31, 3.65s/it] {'loss': 1.2437, 'grad_norm': 0.0007254834613623846, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:46<03:31, 3.65s/it] 89%|████████▉ | 463/520 [28:50<03:28, 3.66s/it] {'loss': 1.064, 'grad_norm': 0.0008082798621789807, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:50<03:28, 3.66s/it] 89%|████████▉ | 464/520 [28:54<03:24, 3.65s/it] {'loss': 1.1872, 'grad_norm': 0.0008050661334190012, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:54<03:24, 3.65s/it] 89%|████████▉ | 465/520 [28:57<03:20, 3.65s/it] {'loss': 1.2916, 'grad_norm': 0.0007996242272804296, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:57<03:20, 3.65s/it] 90%|████████▉ | 466/520 [29:01<03:17, 3.65s/it] {'loss': 1.1825, 'grad_norm': 0.0007446509702296825, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:01<03:17, 3.65s/it] 90%|████████▉ | 467/520 [29:05<03:14, 3.67s/it] {'loss': 1.1328, 'grad_norm': 0.0006944069678573856, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:05<03:14, 3.67s/it] 90%|█████████ | 468/520 [29:08<03:10, 3.66s/it] {'loss': 1.1544, 'grad_norm': 0.0008526701104518368, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:08<03:10, 3.66s/it] 90%|█████████ | 469/520 [29:12<03:06, 3.66s/it] {'loss': 1.2195, 'grad_norm': 0.001030087691270245, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:12<03:06, 3.66s/it] 90%|█████████ | 470/520 [29:16<03:02, 3.65s/it] {'loss': 1.097, 'grad_norm': 0.0007180249979210969, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:16<03:02, 3.65s/it] 91%|█████████ | 471/520 [29:19<02:59, 3.66s/it] {'loss': 1.1207, 'grad_norm': 0.0008126894716496225, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:19<02:59, 3.66s/it] 91%|█████████ | 472/520 [29:23<02:55, 3.66s/it] {'loss': 1.0917, 'grad_norm': 0.0008647165743801865, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:23<02:55, 3.66s/it] 91%|█████████ | 473/520 [29:27<02:52, 3.67s/it] {'loss': 1.1564, 'grad_norm': 0.0008228976303688965, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:27<02:52, 3.67s/it] 91%|█████████ | 474/520 [29:30<02:48, 3.66s/it] {'loss': 1.1692, 'grad_norm': 0.0007268144493475275, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:30<02:48, 3.66s/it] 91%|█████████▏| 475/520 [29:34<02:44, 3.67s/it] {'loss': 1.0858, 'grad_norm': 0.0007383333256496635, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:34<02:44, 3.67s/it] 92%|█████████▏| 476/520 [29:38<02:41, 3.67s/it] {'loss': 1.1429, 'grad_norm': 0.0007781619642249776, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:38<02:41, 3.67s/it] 92%|█████████▏| 477/520 [29:41<02:37, 3.66s/it] {'loss': 1.1403, 'grad_norm': 0.0008876113437704112, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:41<02:37, 3.66s/it] 92%|█████████▏| 478/520 [29:45<02:34, 3.67s/it] 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100%|██████████| 520/520 [32:21<00:00, 3.91s/it] 100%|██████████| 520/520 [32:21<00:00, 3.73s/it] +[2025-10-17 23:40:28,285] [INFO] [launch.py:348:main] Process 745090 exits successfully. +[2025-10-17 23:40:28,286] [INFO] [launch.py:348:main] Process 745086 exits successfully. +[2025-10-17 23:40:28,286] [INFO] [launch.py:348:main] Process 745089 exits successfully. +[2025-10-17 23:40:28,287] [INFO] [launch.py:348:main] Process 745091 exits successfully. +[2025-10-17 23:40:29,288] [INFO] [launch.py:348:main] Process 745085 exits successfully. +[2025-10-17 23:40:29,289] [INFO] [launch.py:348:main] Process 745087 exits successfully. +[2025-10-17 23:40:29,289] [INFO] [launch.py:348:main] Process 745088 exits successfully. +[2025-10-17 23:40:33,294] [INFO] [launch.py:348:main] Process 745084 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.5_2e-1_connector-7.0_2.5_2e-1_ablation_20251017_230637.log +Timestamp: 2025-10-17 23:40:35 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation_20251017_234035.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation_20251017_234035.log new file mode 100644 index 0000000000000000000000000000000000000000..550a161d729247eb7285ee5c888e8bb8991a7002 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation_20251017_234035.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation_20251017_234035.log +Timestamp: 2025-10-17 23:40:35 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:40:38,383] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:41,083] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-17 23:40:41,085] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 2.7 --temperature_mlp_text 2.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 2.7 --temperature_mlp_vision 2.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 2.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:40:43,711] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:44,747] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-17 23:40:44,747] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-17 23:40:44,747] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-17 23:40:44,747] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-17 23:40:44,747] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-17 23:40:44,747] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-17 23:40:44,747] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-17 23:40:44,749] [INFO] [launch.py:253:main] process 766890 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,751] [INFO] [launch.py:253:main] process 766891 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,753] [INFO] [launch.py:253:main] process 766892 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,755] [INFO] [launch.py:253:main] process 766893 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,757] [INFO] [launch.py:253:main] process 766894 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,759] [INFO] [launch.py:253:main] process 766895 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,760] [INFO] [launch.py:253:main] process 766896 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-17 23:40:44,762] [INFO] [launch.py:253:main] process 766897 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-17 23:40:51,581] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,804] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,810] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,812] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,894] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,900] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,901] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:51,902] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-17 23:40:52,000] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,200] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,214] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,215] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,304] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,306] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,306] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-17 23:40:52,306] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-17 23:40:52,307] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.7, 'temperature_mlp': 2.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.7, + "temperature_mlp": 2.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:766890:766890 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:766895:766895 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:766896:766896 [6] NCCL INFO NET/Plugin: Using internal network plugin. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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[2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766891:768503 [1] NCCL INFO ncclCommInitRank comm 0x5640ea27e300 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766892:768500 [2] NCCL INFO ncclCommInitRank comm 0x56495bc19f20 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766893:768499 [3] NCCL INFO ncclCommInitRank comm 0x5632af0678a0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766896:768498 [6] NCCL INFO ncclCommInitRank comm 0x56457a77c990 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766895:768497 [5] NCCL INFO ncclCommInitRank comm 0x55d3e516a9f0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766897:768502 [7] NCCL INFO ncclCommInitRank comm 0x559cdc2599a0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766890:768496 [0] NCCL INFO ncclCommInitRank comm 0x556d2d5fc8c0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:766894:768501 [4] NCCL INFO ncclCommInitRank comm 0x55ea7f340b40 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x4b0e9a08d9b273ab - Init COMPLETE +[2025-10-17 23:41:34,706] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-17 23:41:36,419] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-17 23:41:54,931 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-17 23:41:54,939 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:007->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:766892:773417 [2] NCCL INFO ncclCommInitRank comm 0x7feb3806a700 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766890:773410 [0] NCCL INFO ncclCommInitRank comm 0x7f9b6006b9f0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766896:773411 [6] NCCL INFO ncclCommInitRank comm 0x7fc66406bbc0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766894:773415 [4] NCCL INFO ncclCommInitRank comm 0x7fa39c06b6d0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766895:773416 [5] NCCL INFO ncclCommInitRank comm 0x7f62d006b860 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766891:773412 [1] NCCL INFO ncclCommInitRank comm 0x7f6ac006b030 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766897:773413 [7] NCCL INFO ncclCommInitRank comm 0x7fca5806b110 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x8002f88fa529f8c3 - Init COMPLETE +ywang29-vrdb-test1-worker-0:766893:773414 [3] NCCL INFO ncclCommInitRank comm 0x7f530806bf60 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x8002f88fa529f8c3 - Init COMPLETE + 0%| | 1/520 [00:17<2:32:38, 17.65s/it] {'loss': 2.1257, 'grad_norm': 0.014457811800725353, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:17<2:32:38, 17.65s/it] 0%| | 2/520 [00:21<1:23:22, 9.66s/it] {'loss': 2.1165, 'grad_norm': 0.015486155474120421, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:21<1:23:22, 9.66s/it] 1%| | 3/520 [00:25<1:01:07, 7.09s/it] {'loss': 2.2693, 'grad_norm': 0.017647858906975518, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:25<1:01:07, 7.09s/it] 1%| | 4/520 [00:29<50:35, 5.88s/it] {'loss': 2.1433, 'grad_norm': 0.01462160207832433, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:29<50:35, 5.88s/it] 1%| | 5/520 [00:33<44:45, 5.21s/it] {'loss': 1.7899, 'grad_norm': 0.004864359454102887, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:33<44:45, 5.21s/it] 1%| | 6/520 [00:37<40:40, 4.75s/it] {'loss': 1.5092, 'grad_norm': 0.0034396500657938403, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:37<40:40, 4.75s/it] 1%|▏ | 7/520 [00:41<38:00, 4.45s/it] {'loss': 1.563, 'grad_norm': 0.003782901092945733, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:41<38:00, 4.45s/it] 2%|▏ | 8/520 [00:45<38:05, 4.46s/it] {'loss': 1.5374, 'grad_norm': 0.002779522630437949, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:45<38:05, 4.46s/it] 2%|▏ | 9/520 [00:50<38:04, 4.47s/it] {'loss': 1.5984, 'grad_norm': 0.00303623749554371, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:50<38:04, 4.47s/it] 2%|▏ | 10/520 [00:54<36:40, 4.32s/it] {'loss': 1.4273, 'grad_norm': 0.0024854508068959734, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:54<36:40, 4.32s/it] 2%|▏ | 11/520 [00:58<35:56, 4.24s/it] {'loss': 1.4852, 'grad_norm': 0.0020792511397146043, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:58<35:56, 4.24s/it] 2%|▏ | 12/520 [01:02<35:09, 4.15s/it] {'loss': 1.3849, 'grad_norm': 0.0018371978716899896, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [01:02<35:09, 4.15s/it][2025-10-17 23:43:07,262] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. 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23/520 [01:45<31:54, 3.85s/it] {'loss': 1.4171, 'grad_norm': 0.0014198763192772854, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:45<31:54, 3.85s/it] 5%|▍ | 24/520 [01:49<31:45, 3.84s/it] {'loss': 1.3383, 'grad_norm': 0.0010861243206752504, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:49<31:45, 3.84s/it] 5%|▍ | 25/520 [01:52<31:29, 3.82s/it] {'loss': 1.4014, 'grad_norm': 0.001202910129743401, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:52<31:29, 3.82s/it] 5%|▌ | 26/520 [01:56<31:01, 3.77s/it] {'loss': 1.3632, 'grad_norm': 0.0011128867279990324, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:56<31:01, 3.77s/it] 5%|▌ | 27/520 [02:00<30:40, 3.73s/it] {'loss': 1.2899, 'grad_norm': 0.0012186021459025954, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [02:00<30:40, 3.73s/it] 5%|▌ | 28/520 [02:03<30:28, 3.72s/it] {'loss': 1.3029, 'grad_norm': 0.0011842532134283027, 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3.69s/it] 7%|▋ | 34/520 [02:26<29:48, 3.68s/it] {'loss': 1.2882, 'grad_norm': 0.0011273818683510115, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:26<29:48, 3.68s/it] 7%|▋ | 35/520 [02:29<29:41, 3.67s/it] {'loss': 1.2901, 'grad_norm': 0.001207686921767778, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:29<29:41, 3.67s/it] 7%|▋ | 36/520 [02:33<29:30, 3.66s/it] {'loss': 1.3823, 'grad_norm': 0.0009827295359024354, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:33<29:30, 3.66s/it] 7%|▋ | 37/520 [02:36<29:23, 3.65s/it] {'loss': 1.3754, 'grad_norm': 0.0008765226988881542, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:36<29:23, 3.65s/it] 7%|▋ | 38/520 [02:40<29:17, 3.65s/it] {'loss': 1.4592, 'grad_norm': 0.0009821514538957909, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:40<29:17, 3.65s/it] 8%|▊ | 39/520 [02:44<29:13, 3.65s/it] {'loss': 1.3174, 'grad_norm': 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0.0008855284836922091, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:37<11:08, 3.77s/it] 66%|██████▌ | 344/520 [21:41<10:56, 3.73s/it] {'loss': 1.1287, 'grad_norm': 0.0008303402550740186, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:41<10:56, 3.73s/it] 66%|██████▋ | 345/520 [21:44<10:47, 3.70s/it] {'loss': 1.2307, 'grad_norm': 0.0008759649794909907, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:44<10:47, 3.70s/it] 67%|██████▋ | 346/520 [21:48<10:41, 3.68s/it] {'loss': 1.1704, 'grad_norm': 0.000832487345228819, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:48<10:41, 3.68s/it] 67%|██████▋ | 347/520 [21:52<10:34, 3.67s/it] {'loss': 1.1422, 'grad_norm': 0.0007876253518041808, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:52<10:34, 3.67s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:55<10:29, 3.66s/it] {'loss': 1.1004, 'grad_norm': 0.001119640482510011, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:55<10:29, 3.66s/it] 67%|██████▋ | 349/520 [21:59<10:24, 3.65s/it] {'loss': 1.1371, 'grad_norm': 0.0008243693137031153, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:59<10:24, 3.65s/it] 67%|██████▋ | 350/520 [22:03<10:20, 3.65s/it] {'loss': 1.1816, 'grad_norm': 0.0009628246057514035, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [22:03<10:20, 3.65s/it] 68%|██████▊ | 351/520 [22:06<10:18, 3.66s/it] {'loss': 1.0916, 'grad_norm': 0.0007810251130751715, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:06<10:18, 3.66s/it] 68%|██████▊ | 352/520 [22:10<10:23, 3.71s/it] {'loss': 1.2087, 'grad_norm': 0.0008167805449019431, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:10<10:23, 3.71s/it] 68%|██████▊ | 353/520 [22:14<10:29, 3.77s/it] {'loss': 1.1349, 'grad_norm': 0.0007226642998537687, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:14<10:29, 3.77s/it] 68%|██████▊ | 354/520 [22:18<10:28, 3.79s/it] {'loss': 1.2319, 'grad_norm': 0.0007596072820310931, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:18<10:28, 3.79s/it] 68%|██████▊ | 355/520 [22:22<10:26, 3.80s/it] {'loss': 1.1525, 'grad_norm': 0.0008140645023885902, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:22<10:26, 3.80s/it] 68%|██████▊ | 356/520 [22:25<10:24, 3.81s/it] {'loss': 1.1554, 'grad_norm': 0.0008620739606471316, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:25<10:24, 3.81s/it] 69%|██████▊ | 357/520 [22:29<10:20, 3.81s/it] {'loss': 1.1855, 'grad_norm': 0.0008197443925507919, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:29<10:20, 3.81s/it] 69%|██████▉ | 358/520 [22:33<10:18, 3.82s/it] {'loss': 1.1182, 'grad_norm': 0.0008244832008161165, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:33<10:18, 3.82s/it] 69%|██████▉ | 359/520 [22:37<10:17, 3.83s/it] {'loss': 1.1742, 'grad_norm': 0.0009583005473894209, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:37<10:17, 3.83s/it] 69%|██████▉ | 360/520 [22:41<10:15, 3.84s/it] {'loss': 1.181, 'grad_norm': 0.0008840469303475414, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:41<10:15, 3.84s/it] 69%|██████▉ | 361/520 [22:45<10:10, 3.84s/it] {'loss': 1.1956, 'grad_norm': 0.0007730697325150616, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:45<10:10, 3.84s/it] 70%|██████▉ | 362/520 [22:49<10:06, 3.84s/it] {'loss': 1.1667, 'grad_norm': 0.0008879186488838704, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:49<10:06, 3.84s/it] 70%|██████▉ | 363/520 [22:52<10:00, 3.83s/it] {'loss': 1.1909, 'grad_norm': 0.0008219502555794959, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:52<10:00, 3.83s/it] 70%|███████ | 364/520 [22:56<09:58, 3.83s/it] {'loss': 1.2135, 'grad_norm': 0.0008035995088749398, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:56<09:58, 3.83s/it] 70%|███████ | 365/520 [23:00<09:54, 3.83s/it] {'loss': 1.245, 'grad_norm': 0.000865289398686596, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [23:00<09:54, 3.83s/it] 70%|███████ | 366/520 [23:04<09:50, 3.84s/it] {'loss': 1.207, 'grad_norm': 0.0008015231248501337, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [23:04<09:50, 3.84s/it] 71%|███████ | 367/520 [23:08<09:46, 3.83s/it] {'loss': 1.2088, 'grad_norm': 0.0008404843995267444, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:08<09:46, 3.83s/it] 71%|███████ | 368/520 [23:12<09:41, 3.83s/it] {'loss': 1.0606, 'grad_norm': 0.0008598537087447229, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:12<09:41, 3.83s/it] 71%|███████ | 369/520 [23:15<09:37, 3.82s/it] {'loss': 1.1686, 'grad_norm': 0.0007241149008780517, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:15<09:37, 3.82s/it] 71%|███████ | 370/520 [23:19<09:34, 3.83s/it] {'loss': 1.1223, 'grad_norm': 0.000787209823133632, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:19<09:34, 3.83s/it] 71%|███████▏ | 371/520 [23:23<09:28, 3.82s/it] {'loss': 1.1195, 'grad_norm': 0.0008552674905806826, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:23<09:28, 3.82s/it] 72%|███████▏ | 372/520 [23:27<09:25, 3.82s/it] {'loss': 1.238, 'grad_norm': 0.000961029615155767, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:27<09:25, 3.82s/it] 72%|███████▏ | 373/520 [23:31<09:21, 3.82s/it] {'loss': 1.1292, 'grad_norm': 0.0008595186584209626, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:31<09:21, 3.82s/it] 72%|███████▏ | 374/520 [23:34<09:18, 3.82s/it] {'loss': 1.2083, 'grad_norm': 0.0008399402204265849, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:34<09:18, 3.82s/it] 72%|███████▏ | 375/520 [23:38<09:10, 3.80s/it] {'loss': 1.1271, 'grad_norm': 0.000888640555224068, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:38<09:10, 3.80s/it] 72%|███████▏ | 376/520 [23:42<09:01, 3.76s/it] {'loss': 1.2337, 'grad_norm': 0.0008100499980345634, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:42<09:01, 3.76s/it] 72%|███████▎ | 377/520 [23:45<08:52, 3.72s/it] {'loss': 1.1643, 'grad_norm': 0.0008585288366492498, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:45<08:52, 3.72s/it] 73%|███████▎ | 378/520 [23:49<08:45, 3.70s/it] {'loss': 1.2279, 'grad_norm': 0.0008013760795784246, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:49<08:45, 3.70s/it] 73%|███████▎ | 379/520 [23:53<08:38, 3.68s/it] {'loss': 1.1991, 'grad_norm': 0.0007992782994960284, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:53<08:38, 3.68s/it] 73%|███████▎ | 380/520 [23:56<08:32, 3.66s/it] {'loss': 1.2143, 'grad_norm': 0.0008273076885495454, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:56<08:32, 3.66s/it] 73%|███████▎ | 381/520 [24:00<08:27, 3.65s/it] {'loss': 1.2044, 'grad_norm': 0.0007941244352552196, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [24:00<08:27, 3.65s/it] 73%|███████▎ | 382/520 [24:04<08:24, 3.65s/it] {'loss': 1.1851, 'grad_norm': 0.0008249998572160992, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [24:04<08:24, 3.65s/it] 74%|███████▎ | 383/520 [24:07<08:19, 3.64s/it] {'loss': 1.0463, 'grad_norm': 0.0009624214783635842, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [24:07<08:19, 3.64s/it] 74%|███████▍ | 384/520 [24:11<08:14, 3.64s/it] {'loss': 1.2185, 'grad_norm': 0.000790164218588497, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:11<08:14, 3.64s/it] 74%|███████▍ | 385/520 [24:15<08:11, 3.64s/it] {'loss': 1.1871, 'grad_norm': 0.000765857405278018, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:15<08:11, 3.64s/it] 74%|███████▍ | 386/520 [24:18<08:07, 3.64s/it] {'loss': 1.1395, 'grad_norm': 0.0007646526496194106, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:18<08:07, 3.64s/it] 74%|███████▍ | 387/520 [24:22<08:03, 3.64s/it] {'loss': 1.24, 'grad_norm': 0.0008535053707408264, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:22<08:03, 3.64s/it] 75%|███████▍ | 388/520 [24:25<08:00, 3.64s/it] {'loss': 1.0959, 'grad_norm': 0.0007950918384579662, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:25<08:00, 3.64s/it] 75%|███████▍ | 389/520 [24:29<07:56, 3.63s/it] {'loss': 1.1407, 'grad_norm': 0.0009613354564315764, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:29<07:56, 3.63s/it] 75%|███████▌ | 390/520 [24:33<07:53, 3.64s/it] {'loss': 1.2076, 'grad_norm': 0.0008039857030998737, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:33<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:36<07:49, 3.64s/it] {'loss': 1.2756, 'grad_norm': 0.0008913504366736148, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:36<07:49, 3.64s/it] 75%|███████▌ | 392/520 [24:40<07:45, 3.64s/it] {'loss': 1.0994, 'grad_norm': 0.0008605830206923262, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:40<07:45, 3.64s/it] 76%|███████▌ | 393/520 [24:44<07:47, 3.68s/it] {'loss': 1.0918, 'grad_norm': 0.0006852547988854827, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:44<07:47, 3.68s/it] 76%|███████▌ | 394/520 [24:48<07:45, 3.70s/it] {'loss': 1.1648, 'grad_norm': 0.0008707932936720919, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:48<07:45, 3.70s/it] 76%|███████▌ | 395/520 [24:51<07:47, 3.74s/it] {'loss': 1.1296, 'grad_norm': 0.0008963090678814895, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:51<07:47, 3.74s/it] 76%|███████▌ | 396/520 [24:55<07:47, 3.77s/it] {'loss': 1.2077, 'grad_norm': 0.0008848077292750852, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:55<07:47, 3.77s/it] 76%|███████▋ | 397/520 [24:59<07:46, 3.79s/it] {'loss': 1.1853, 'grad_norm': 0.0007929832551044366, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:59<07:46, 3.79s/it] 77%|███████▋ | 398/520 [25:03<07:38, 3.76s/it] {'loss': 1.186, 'grad_norm': 0.0008814124182299401, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [25:03<07:38, 3.76s/it] 77%|███████▋ | 399/520 [25:06<07:31, 3.73s/it] {'loss': 1.1308, 'grad_norm': 0.0007895277501202218, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [25:06<07:31, 3.73s/it] 77%|███████▋ | 400/520 [25:10<07:27, 3.73s/it] {'loss': 1.1605, 'grad_norm': 0.0007436343698456078, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:10<07:27, 3.73s/it] 77%|███████▋ | 401/520 [25:14<07:21, 3.71s/it] {'loss': 1.0212, 'grad_norm': 0.0009373565507976764, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:14<07:21, 3.71s/it] 77%|███████▋ | 402/520 [25:17<07:15, 3.69s/it] {'loss': 1.1468, 'grad_norm': 0.000870368438415476, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:17<07:15, 3.69s/it] 78%|███████▊ | 403/520 [25:21<07:10, 3.68s/it] {'loss': 1.1706, 'grad_norm': 0.0009053759044882227, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:21<07:10, 3.68s/it] 78%|███████▊ | 404/520 [25:25<07:05, 3.67s/it] {'loss': 1.0822, 'grad_norm': 0.0009569425117136927, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:25<07:05, 3.67s/it] 78%|███████▊ | 405/520 [25:28<07:01, 3.67s/it] {'loss': 1.1434, 'grad_norm': 0.0008240631474208924, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:28<07:01, 3.67s/it] 78%|███████▊ | 406/520 [25:32<06:58, 3.67s/it] {'loss': 1.0588, 'grad_norm': 0.0012208509930978569, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:32<06:58, 3.67s/it] 78%|███████▊ | 407/520 [25:36<06:54, 3.67s/it] {'loss': 1.2473, 'grad_norm': 0.0009011232072755392, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:36<06:54, 3.67s/it] 78%|███████▊ | 408/520 [25:39<06:49, 3.66s/it] {'loss': 1.163, 'grad_norm': 0.0010570781368430986, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:39<06:49, 3.66s/it] 79%|███████▊ | 409/520 [25:43<06:47, 3.67s/it] {'loss': 1.273, 'grad_norm': 0.0008986870468212459, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:43<06:47, 3.67s/it] 79%|███████▉ | 410/520 [25:47<06:42, 3.66s/it] {'loss': 1.0177, 'grad_norm': 0.0008971639735819244, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:47<06:42, 3.66s/it] 79%|███████▉ | 411/520 [25:50<06:38, 3.66s/it] {'loss': 1.2573, 'grad_norm': 0.000928848907332058, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:50<06:38, 3.66s/it] 79%|███████▉ | 412/520 [25:54<06:35, 3.66s/it] {'loss': 1.1665, 'grad_norm': 0.0008313609830732859, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:54<06:35, 3.66s/it] 79%|███████▉ | 413/520 [25:58<06:32, 3.67s/it] {'loss': 1.1515, 'grad_norm': 0.0007656958724921956, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:58<06:32, 3.67s/it] 80%|███████▉ | 414/520 [26:01<06:28, 3.67s/it] {'loss': 0.9685, 'grad_norm': 0.0007097998659494523, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [26:01<06:28, 3.67s/it] 80%|███████▉ | 415/520 [26:05<06:25, 3.67s/it] {'loss': 1.1494, 'grad_norm': 0.0007926009193962349, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [26:05<06:25, 3.67s/it] 80%|████████ | 416/520 [26:09<06:21, 3.67s/it] {'loss': 1.0601, 'grad_norm': 0.0008967037182308413, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:09<06:21, 3.67s/it] 80%|████████ | 417/520 [26:12<06:17, 3.67s/it] {'loss': 1.2178, 'grad_norm': 0.000897998459653111, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:12<06:17, 3.67s/it] 80%|████████ | 418/520 [26:16<06:13, 3.66s/it] {'loss': 1.2113, 'grad_norm': 0.0008168528553594371, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:16<06:13, 3.66s/it] 81%|████████ | 419/520 [26:20<06:10, 3.67s/it] {'loss': 1.2051, 'grad_norm': 0.000897773808600241, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:20<06:10, 3.67s/it] 81%|████████ | 420/520 [26:23<06:06, 3.67s/it] {'loss': 1.0982, 'grad_norm': 0.0008923458855653109, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:23<06:06, 3.67s/it] 81%|████████ | 421/520 [26:27<06:02, 3.66s/it] {'loss': 1.0323, 'grad_norm': 0.0009919259626159302, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:27<06:02, 3.66s/it] 81%|████████ | 422/520 [26:31<05:58, 3.66s/it] {'loss': 1.1531, 'grad_norm': 0.0008596212426741874, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:31<05:58, 3.66s/it] 81%|████████▏ | 423/520 [26:34<05:55, 3.66s/it] {'loss': 1.1276, 'grad_norm': 0.0009117268118377971, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:34<05:55, 3.66s/it] 82%|████████▏ | 424/520 [26:38<05:51, 3.66s/it] {'loss': 1.2384, 'grad_norm': 0.0008251518459290918, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:38<05:51, 3.66s/it] 82%|████████▏ | 425/520 [26:42<05:47, 3.66s/it] {'loss': 1.1448, 'grad_norm': 0.0007993503087239842, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:42<05:47, 3.66s/it] 82%|████████▏ | 426/520 [26:45<05:43, 3.66s/it] {'loss': 1.1633, 'grad_norm': 0.0010393391766225655, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:45<05:43, 3.66s/it] 82%|████████▏ | 427/520 [26:49<05:39, 3.66s/it] {'loss': 1.076, 'grad_norm': 0.0007922189734183968, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:49<05:39, 3.66s/it] 82%|████████▏ | 428/520 [26:53<05:37, 3.67s/it] {'loss': 1.0628, 'grad_norm': 0.0008761587940480457, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:53<05:37, 3.67s/it] 82%|████████▎ | 429/520 [26:56<05:33, 3.66s/it] {'loss': 1.1554, 'grad_norm': 0.0008672811673498299, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:56<05:33, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [27:00<05:29, 3.66s/it] {'loss': 1.1584, 'grad_norm': 0.0007716035627644267, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [27:00<05:29, 3.66s/it] 83%|████████▎ | 431/520 [27:04<05:26, 3.66s/it] {'loss': 1.1247, 'grad_norm': 0.0009199090836529961, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [27:04<05:26, 3.66s/it] 83%|████████▎ | 432/520 [27:07<05:22, 3.67s/it] {'loss': 1.0699, 'grad_norm': 0.0008433376446214526, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:07<05:22, 3.67s/it] 83%|████████▎ | 433/520 [27:11<05:18, 3.66s/it] {'loss': 1.1988, 'grad_norm': 0.0008562586069945131, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:11<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:15<05:16, 3.68s/it] {'loss': 0.9508, 'grad_norm': 0.0008203719714238817, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:15<05:16, 3.68s/it] 84%|████████▎ | 435/520 [27:18<05:11, 3.67s/it] {'loss': 1.2322, 'grad_norm': 0.0009461961365800766, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:18<05:11, 3.67s/it] 84%|████████▍ | 436/520 [27:22<05:07, 3.66s/it] {'loss': 1.0404, 'grad_norm': 0.0008585141308043646, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:22<05:07, 3.66s/it] 84%|████████▍ | 437/520 [27:26<05:03, 3.66s/it] {'loss': 1.2525, 'grad_norm': 0.000849298334708079, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:26<05:03, 3.66s/it] 84%|████████▍ | 438/520 [27:29<04:59, 3.65s/it] {'loss': 1.0773, 'grad_norm': 0.0008379168467754905, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:29<04:59, 3.65s/it] 84%|████████▍ | 439/520 [27:33<04:55, 3.65s/it] {'loss': 1.1091, 'grad_norm': 0.0006927313057130754, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:33<04:55, 3.65s/it] 85%|████████▍ | 440/520 [27:37<04:51, 3.65s/it] {'loss': 1.1072, 'grad_norm': 0.0008655699591519428, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:37<04:51, 3.65s/it] 85%|████████▍ | 441/520 [27:40<04:48, 3.65s/it] {'loss': 1.1232, 'grad_norm': 0.0009340366755783335, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:40<04:48, 3.65s/it] 85%|████████▌ | 442/520 [27:44<04:44, 3.65s/it] {'loss': 1.1754, 'grad_norm': 0.0009230126249664404, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:44<04:44, 3.65s/it] 85%|████████▌ | 443/520 [27:47<04:40, 3.65s/it] {'loss': 1.1861, 'grad_norm': 0.0008187337320129074, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:47<04:40, 3.65s/it] 85%|████████▌ | 444/520 [27:51<04:36, 3.64s/it] {'loss': 1.1505, 'grad_norm': 0.0007601403676669812, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:51<04:36, 3.64s/it] 86%|████████▌ | 445/520 [27:55<04:32, 3.64s/it] {'loss': 1.0798, 'grad_norm': 0.0008167034264291265, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:55<04:32, 3.64s/it] 86%|████████▌ | 446/520 [27:58<04:29, 3.64s/it] {'loss': 1.1988, 'grad_norm': 0.0007579705196159004, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:58<04:29, 3.64s/it] 86%|████████▌ | 447/520 [28:02<04:26, 3.65s/it] {'loss': 1.152, 'grad_norm': 0.0008039247315466717, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [28:02<04:26, 3.65s/it] 86%|████████▌ | 448/520 [28:06<04:23, 3.66s/it] {'loss': 1.1497, 'grad_norm': 0.0008936623087889952, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [28:06<04:23, 3.66s/it] 86%|████████▋ | 449/520 [28:09<04:19, 3.66s/it] {'loss': 1.16, 'grad_norm': 0.00084387028928524, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:09<04:19, 3.66s/it] 87%|████████▋ | 450/520 [28:13<04:16, 3.67s/it] {'loss': 1.1742, 'grad_norm': 0.0008226197023409753, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:13<04:16, 3.67s/it] 87%|████████▋ | 451/520 [28:17<04:12, 3.67s/it] {'loss': 1.1758, 'grad_norm': 0.0008531078162883593, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:17<04:12, 3.67s/it] 87%|████████▋ | 452/520 [28:20<04:09, 3.67s/it] {'loss': 1.2024, 'grad_norm': 0.0007700898906155614, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:20<04:09, 3.67s/it] 87%|████████▋ | 453/520 [28:24<04:05, 3.67s/it] {'loss': 1.1776, 'grad_norm': 0.0008236563422951039, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:24<04:05, 3.67s/it] 87%|████████▋ | 454/520 [28:28<04:02, 3.67s/it] {'loss': 1.0851, 'grad_norm': 0.0008749342579005384, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:28<04:02, 3.67s/it] 88%|████████▊ | 455/520 [28:31<03:57, 3.66s/it] {'loss': 1.2261, 'grad_norm': 0.0008334891298662036, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:31<03:57, 3.66s/it] 88%|████████▊ | 456/520 [28:35<03:54, 3.66s/it] {'loss': 1.1554, 'grad_norm': 0.0008427618972562397, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:35<03:54, 3.66s/it] 88%|████████▊ | 457/520 [28:39<03:50, 3.65s/it] {'loss': 1.0768, 'grad_norm': 0.0007347201990374091, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:39<03:50, 3.65s/it] 88%|████████▊ | 458/520 [28:42<03:46, 3.65s/it] {'loss': 1.2748, 'grad_norm': 0.0009194971040123589, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:42<03:46, 3.65s/it] 88%|████████▊ | 459/520 [28:46<03:43, 3.66s/it] {'loss': 1.2102, 'grad_norm': 0.0009329084798372966, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:46<03:43, 3.66s/it] 88%|████████▊ | 460/520 [28:50<03:39, 3.66s/it] {'loss': 1.1017, 'grad_norm': 0.0008274219488923329, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:50<03:39, 3.66s/it] 89%|████████▊ | 461/520 [28:53<03:35, 3.66s/it] {'loss': 1.1576, 'grad_norm': 0.000644646535643413, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:53<03:35, 3.66s/it] 89%|████████▉ | 462/520 [28:57<03:31, 3.65s/it] {'loss': 1.2482, 'grad_norm': 0.0008071274012849331, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:57<03:31, 3.65s/it] 89%|████████▉ | 463/520 [29:01<03:27, 3.65s/it] {'loss': 1.0632, 'grad_norm': 0.0008586401053253416, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 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520/520 [32:30<00:00, 3.87s/it] 100%|██████████| 520/520 [32:30<00:00, 3.75s/it] +[2025-10-18 00:14:36,912] [INFO] [launch.py:348:main] Process 766897 exits successfully. +[2025-10-18 00:14:37,913] [INFO] [launch.py:348:main] Process 766896 exits successfully. +[2025-10-18 00:14:37,914] [INFO] [launch.py:348:main] Process 766891 exits successfully. +[2025-10-18 00:14:37,914] [INFO] [launch.py:348:main] Process 766893 exits successfully. +[2025-10-18 00:14:38,916] [INFO] [launch.py:348:main] Process 766894 exits successfully. +[2025-10-18 00:14:38,916] [INFO] [launch.py:348:main] Process 766892 exits successfully. +[2025-10-18 00:14:38,917] [INFO] [launch.py:348:main] Process 766895 exits successfully. +[2025-10-18 00:14:42,922] [INFO] [launch.py:348:main] Process 766890 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.7_2e-1_connector-7.0_2.7_2e-1_ablation_20251017_234035.log +Timestamp: 2025-10-18 00:14:45 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation_20251018_001445.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation_20251018_001445.log new file mode 100644 index 0000000000000000000000000000000000000000..8ab7cbd0bde9bba70ef2d96ef6d7187036ee207c --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation_20251018_001445.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation_20251018_001445.log +Timestamp: 2025-10-18 00:14:45 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:14:48,111] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:14:51,051] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 00:14:51,053] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 7.0 --temperature_attn_text 2.9 --temperature_mlp_text 2.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 7.0 --temperature_attn_vision 2.9 --temperature_mlp_vision 2.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 7.0 --temperature_connector 2.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:14:53,616] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:14:54,636] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 00:14:54,636] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 00:14:54,636] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 00:14:54,636] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 00:14:54,636] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 00:14:54,636] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 00:14:54,636] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 00:14:54,638] [INFO] [launch.py:253:main] process 788726 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,640] [INFO] [launch.py:253:main] process 788727 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,642] [INFO] [launch.py:253:main] process 788728 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,644] [INFO] [launch.py:253:main] process 788729 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,646] [INFO] [launch.py:253:main] process 788730 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,648] [INFO] [launch.py:253:main] process 788731 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,650] [INFO] [launch.py:253:main] process 788732 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:14:54,652] [INFO] [launch.py:253:main] process 788733 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '7.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '7.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '7.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:15:01,372] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,626] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,693] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,698] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,698] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,707] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,730] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,730] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:15:01,785] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,048] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,094] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,102] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,102] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,102] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 00:15:02,120] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,137] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:15:02,138] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.9, 'temperature_mlp': 2.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.9, + "temperature_mlp": 2.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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+ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788727:790318 [1] NCCL INFO ncclCommInitRank comm 0x55f735f3a0a0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788730:790338 [4] NCCL INFO ncclCommInitRank comm 0x558e03d32600 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788731:790319 [5] NCCL INFO ncclCommInitRank comm 0x56340930fa40 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788732:790316 [6] NCCL INFO ncclCommInitRank comm 0x560f24ed7f70 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788726:790315 [0] NCCL INFO ncclCommInitRank comm 0x55e18e789950 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788728:790339 [2] NCCL INFO ncclCommInitRank comm 0x55ec166bf250 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:788729:790317 [3] NCCL INFO ncclCommInitRank comm 0x55aa91ee56d0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xdf0108b4a64e2986 - Init COMPLETE +ywang29-vrdb-test1-worker-0:788733:790337 [7] NCCL INFO ncclCommInitRank comm 0x56458d8eb000 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xdf0108b4a64e2986 - Init COMPLETE +[2025-10-18 00:15:45,863] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 00:15:47,648] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=7.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=7.000000 +Pre-training init connector._connector.0.scores: Mean=7.000005 +Pre-training init connector._connector.2.scores: Mean=6.999971 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 00:16:06,045 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 00:16:06,052 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:007->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:788726:795282 [0] NCCL INFO ncclCommInitRank comm 0x7f8ac406b400 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788732:795284 [6] NCCL INFO ncclCommInitRank comm 0x7fc85006b060 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788730:795287 [4] NCCL INFO ncclCommInitRank comm 0x7f97f806a660 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788728:795286 [2] NCCL INFO ncclCommInitRank comm 0x7f45c806ab60 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788729:795285 [3] NCCL INFO ncclCommInitRank comm 0x7f604c06ae20 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788733:795289 [7] NCCL INFO ncclCommInitRank comm 0x7ff45c06ac60 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788731:795288 [5] NCCL INFO ncclCommInitRank comm 0x7fc82c06afc0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x305c1cdc0ac044cb - Init COMPLETE +ywang29-vrdb-test1-worker-0:788727:795283 [1] NCCL INFO ncclCommInitRank comm 0x7f97ec06b400 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x305c1cdc0ac044cb - Init COMPLETE + 0%| | 1/520 [00:14<2:08:56, 14.91s/it] {'loss': 2.1525, 'grad_norm': 0.015606007889192596, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:08:56, 14.91s/it] 0%| | 2/520 [00:18<1:12:22, 8.38s/it] {'loss': 2.1364, 'grad_norm': 0.016651940702066766, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:12:22, 8.38s/it] 1%| | 3/520 [00:22<54:14, 6.29s/it] {'loss': 2.2931, 'grad_norm': 0.01894175584372754, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<54:14, 6.29s/it] 1%| | 4/520 [00:26<45:37, 5.30s/it] {'loss': 2.1734, 'grad_norm': 0.015588342996622671, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:26<45:37, 5.30s/it] 1%| | 5/520 [00:30<40:51, 4.76s/it] {'loss': 1.768, 'grad_norm': 0.005461688839985132, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:30<40:51, 4.76s/it] 1%| | 6/520 [00:33<38:09, 4.45s/it] {'loss': 1.5266, 'grad_norm': 0.0038568287614613376, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<38:09, 4.45s/it] 1%|▏ | 7/520 [00:37<36:02, 4.22s/it] {'loss': 1.5595, 'grad_norm': 0.003955520618424513, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:37<36:02, 4.22s/it] 2%|▏ | 8/520 [00:41<36:02, 4.22s/it] {'loss': 1.5206, 'grad_norm': 0.002905691114267165, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:41<36:02, 4.22s/it] 2%|▏ | 9/520 [00:46<35:42, 4.19s/it] {'loss': 1.6177, 'grad_norm': 0.00350610997959845, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:46<35:42, 4.19s/it] 2%|▏ | 10/520 [00:49<34:01, 4.00s/it] {'loss': 1.4326, 'grad_norm': 0.002818307944748607, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:49<34:01, 4.00s/it] 2%|▏ | 11/520 [00:53<33:15, 3.92s/it] {'loss': 1.4809, 'grad_norm': 0.0022523766518906183, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:53<33:15, 3.92s/it] 2%|▏ | 12/520 [00:57<32:30, 3.84s/it] {'loss': 1.3837, 'grad_norm': 0.00205086331203838, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:57<32:30, 3.84s/it][2025-10-18 00:17:11,716] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:01<33:45, 4.00s/it] {'loss': 1.4202, 'grad_norm': 0.002405720296346626, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:01<33:45, 4.00s/it] 3%|▎ | 14/520 [01:05<33:01, 3.92s/it] {'loss': 1.4677, 'grad_norm': 0.0024328795603251294, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:05<33:01, 3.92s/it] 3%|▎ | 15/520 [01:08<32:32, 3.87s/it] {'loss': 1.4215, 'grad_norm': 0.0014773606197074373, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:08<32:32, 3.87s/it] 3%|▎ | 16/520 [01:12<32:05, 3.82s/it] {'loss': 1.3795, 'grad_norm': 0.001562162466893213, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:12<32:05, 3.82s/it] 3%|▎ | 17/520 [01:16<31:47, 3.79s/it] {'loss': 1.4835, 'grad_norm': 0.0019237319922586513, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:16<31:47, 3.79s/it] 3%|▎ | 18/520 [01:20<31:37, 3.78s/it] {'loss': 1.3397, 'grad_norm': 0.0018503869736199974, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:20<31:37, 3.78s/it] 4%|▎ | 19/520 [01:23<31:26, 3.77s/it] {'loss': 1.3746, 'grad_norm': 0.0013891617087225252, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:23<31:26, 3.77s/it] 4%|▍ | 20/520 [01:27<31:18, 3.76s/it] {'loss': 1.3092, 'grad_norm': 0.0013687577186935692, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:27<31:18, 3.76s/it] 4%|▍ | 21/520 [01:31<31:16, 3.76s/it] {'loss': 1.3744, 'grad_norm': 0.0017965589159438166, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:31<31:16, 3.76s/it] 4%|▍ | 22/520 [01:35<31:12, 3.76s/it] {'loss': 1.4653, 'grad_norm': 0.0014820681558835164, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:35<31:12, 3.76s/it] 4%|▍ | 23/520 [01:38<31:10, 3.76s/it] {'loss': 1.4091, 'grad_norm': 0.001406910345870914, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:38<31:10, 3.76s/it] 5%|▍ | 24/520 [01:42<31:15, 3.78s/it] {'loss': 1.3402, 'grad_norm': 0.001167035042291257, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:42<31:15, 3.78s/it] 5%|▍ | 25/520 [01:46<30:50, 3.74s/it] {'loss': 1.3991, 'grad_norm': 0.0012989068122326059, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:46<30:50, 3.74s/it] 5%|▌ | 26/520 [01:49<30:34, 3.71s/it] {'loss': 1.3706, 'grad_norm': 0.0012414526911725791, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:49<30:34, 3.71s/it] 5%|▌ | 27/520 [01:53<30:26, 3.70s/it] {'loss': 1.2879, 'grad_norm': 0.0011705204186265247, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:53<30:26, 3.70s/it] 5%|▌ | 28/520 [01:57<30:18, 3.70s/it] {'loss': 1.3014, 'grad_norm': 0.0011300629938303937, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:57<30:18, 3.70s/it] 6%|▌ | 29/520 [02:00<30:03, 3.67s/it] {'loss': 1.3315, 'grad_norm': 0.0012033188410321234, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [02:00<30:03, 3.67s/it] 6%|▌ | 30/520 [02:04<29:52, 3.66s/it] {'loss': 1.4156, 'grad_norm': 0.0011308400429682949, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:04<29:52, 3.66s/it] 6%|▌ | 31/520 [02:08<29:49, 3.66s/it] {'loss': 1.2969, 'grad_norm': 0.0011320202961419988, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:08<29:49, 3.66s/it] 6%|▌ | 32/520 [02:11<29:40, 3.65s/it] {'loss': 1.256, 'grad_norm': 0.0010525664402134822, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:11<29:40, 3.65s/it] 6%|▋ | 33/520 [02:15<29:37, 3.65s/it] {'loss': 1.2972, 'grad_norm': 0.0011340266604064797, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:15<29:37, 3.65s/it] 7%|▋ | 34/520 [02:19<29:31, 3.65s/it] {'loss': 1.2907, 'grad_norm': 0.0012399442057773435, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:19<29:31, 3.65s/it] 7%|▋ | 35/520 [02:22<29:29, 3.65s/it] {'loss': 1.2957, 'grad_norm': 0.0013653240679477566, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:22<29:29, 3.65s/it] 7%|▋ | 36/520 [02:26<29:22, 3.64s/it] {'loss': 1.3867, 'grad_norm': 0.001014717748739929, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:26<29:22, 3.64s/it] 7%|▋ | 37/520 [02:30<29:19, 3.64s/it] {'loss': 1.3806, 'grad_norm': 0.0009881168857145298, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:30<29:19, 3.64s/it] 7%|▋ | 38/520 [02:33<29:11, 3.63s/it] {'loss': 1.4616, 'grad_norm': 0.0010845896091666576, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:33<29:11, 3.63s/it] 8%|▊ | 39/520 [02:37<29:04, 3.63s/it] {'loss': 1.316, 'grad_norm': 0.001203849071779959, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:37<29:04, 3.63s/it] 8%|▊ | 40/520 [02:40<28:57, 3.62s/it] {'loss': 1.3509, 'grad_norm': 0.0009679223260640837, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:40<28:57, 3.62s/it] 8%|▊ | 41/520 [02:44<28:59, 3.63s/it] {'loss': 1.3311, 'grad_norm': 0.0010202543076878452, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:44<28:59, 3.63s/it] 8%|▊ | 42/520 [02:48<28:52, 3.63s/it] {'loss': 1.3293, 'grad_norm': 0.0012803236148309963, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:48<28:52, 3.63s/it] 8%|▊ | 43/520 [02:51<28:52, 3.63s/it] {'loss': 1.2779, 'grad_norm': 0.0010360012526135654, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:51<28:52, 3.63s/it] 8%|▊ | 44/520 [02:55<28:53, 3.64s/it] {'loss': 1.3698, 'grad_norm': 0.0009958460084691602, 'learning_rate': 0.19848077530122082, 'epoch': 0.08} + 8%|▊ 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'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:12<11:26, 3.77s/it] 65%|██████▌ | 339/520 [21:16<11:24, 3.78s/it] {'loss': 1.1546, 'grad_norm': 0.0008700943260127261, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:16<11:24, 3.78s/it] 65%|██████▌ | 340/520 [21:20<11:22, 3.79s/it] {'loss': 1.1493, 'grad_norm': 0.000893302148261642, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:20<11:22, 3.79s/it] 66%|██████▌ | 341/520 [21:23<11:12, 3.76s/it] {'loss': 1.1738, 'grad_norm': 0.0009232020061708653, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:23<11:12, 3.76s/it] 66%|██████▌ | 342/520 [21:27<11:01, 3.72s/it] {'loss': 1.1972, 'grad_norm': 0.0011622887206237968, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:27<11:01, 3.72s/it] 66%|██████▌ | 343/520 [21:31<10:54, 3.70s/it] {'loss': 1.1527, 'grad_norm': 0.0010143338530636127, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:31<10:54, 3.70s/it] 66%|██████▌ | 344/520 [21:34<10:48, 3.68s/it] {'loss': 1.13, 'grad_norm': 0.0009070067309947753, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:34<10:48, 3.68s/it] 66%|██████▋ | 345/520 [21:38<10:41, 3.67s/it] {'loss': 1.2332, 'grad_norm': 0.0009383070272920811, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:38<10:41, 3.67s/it] 67%|██████▋ | 346/520 [21:42<10:47, 3.72s/it] {'loss': 1.1742, 'grad_norm': 0.0009284039822394016, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:42<10:47, 3.72s/it] 67%|██████▋ | 347/520 [21:46<10:48, 3.75s/it] {'loss': 1.1432, 'grad_norm': 0.0008197007410034633, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:46<10:48, 3.75s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:49<10:48, 3.77s/it] {'loss': 1.1009, 'grad_norm': 0.0011567208883712947, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:49<10:48, 3.77s/it] 67%|██████▋ | 349/520 [21:53<10:47, 3.79s/it] {'loss': 1.1392, 'grad_norm': 0.0008712804064612935, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:53<10:47, 3.79s/it] 67%|██████▋ | 350/520 [21:57<10:46, 3.80s/it] {'loss': 1.1828, 'grad_norm': 0.0009490938838257018, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:57<10:46, 3.80s/it] 68%|██████▊ | 351/520 [22:01<10:44, 3.82s/it] {'loss': 1.093, 'grad_norm': 0.0008267394253269477, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:01<10:44, 3.82s/it] 68%|██████▊ | 352/520 [22:05<10:40, 3.82s/it] {'loss': 1.2109, 'grad_norm': 0.0008741530948431925, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:05<10:40, 3.82s/it] 68%|██████▊ | 353/520 [22:09<10:38, 3.83s/it] {'loss': 1.1353, 'grad_norm': 0.0007539230745649878, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:09<10:38, 3.83s/it] 68%|██████▊ | 354/520 [22:12<10:35, 3.83s/it] {'loss': 1.2363, 'grad_norm': 0.0008045415558406649, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:12<10:35, 3.83s/it] 68%|██████▊ | 355/520 [22:16<10:26, 3.80s/it] {'loss': 1.1542, 'grad_norm': 0.0008531816192055247, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:16<10:26, 3.80s/it] 68%|██████▊ | 356/520 [22:20<10:18, 3.77s/it] {'loss': 1.1564, 'grad_norm': 0.0008934157971492184, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:20<10:18, 3.77s/it] 69%|██████▊ | 357/520 [22:24<10:18, 3.79s/it] {'loss': 1.1846, 'grad_norm': 0.000855208395635859, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:24<10:18, 3.79s/it] 69%|██████▉ | 358/520 [22:28<10:15, 3.80s/it] {'loss': 1.1201, 'grad_norm': 0.0008880326757017079, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:28<10:15, 3.80s/it] 69%|██████▉ | 359/520 [22:31<10:13, 3.81s/it] {'loss': 1.176, 'grad_norm': 0.0009830031642732763, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:31<10:13, 3.81s/it] 69%|██████▉ | 360/520 [22:35<10:05, 3.79s/it] {'loss': 1.1853, 'grad_norm': 0.0009694588909110467, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:35<10:05, 3.79s/it] 69%|██████▉ | 361/520 [22:39<09:57, 3.76s/it] {'loss': 1.1977, 'grad_norm': 0.0008072245328886048, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:39<09:57, 3.76s/it] 70%|██████▉ | 362/520 [22:43<09:50, 3.74s/it] {'loss': 1.1682, 'grad_norm': 0.0009173362159006837, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:43<09:50, 3.74s/it] 70%|██████▉ | 363/520 [22:46<09:43, 3.71s/it] {'loss': 1.1919, 'grad_norm': 0.0008709102281118572, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:46<09:43, 3.71s/it] 70%|███████ | 364/520 [22:50<09:37, 3.70s/it] {'loss': 1.2173, 'grad_norm': 0.0008559239500562224, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:50<09:37, 3.70s/it] 70%|███████ | 365/520 [22:53<09:31, 3.69s/it] {'loss': 1.2488, 'grad_norm': 0.0009185588659459452, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:53<09:31, 3.69s/it] 70%|███████ | 366/520 [22:57<09:25, 3.67s/it] {'loss': 1.2071, 'grad_norm': 0.0008584445246814075, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:57<09:25, 3.67s/it] 71%|███████ | 367/520 [23:01<09:20, 3.66s/it] {'loss': 1.2099, 'grad_norm': 0.00089073907539288, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:01<09:20, 3.66s/it] 71%|███████ | 368/520 [23:04<09:15, 3.66s/it] {'loss': 1.0624, 'grad_norm': 0.0009100776304003592, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:04<09:15, 3.66s/it] 71%|███████ | 369/520 [23:08<09:11, 3.65s/it] {'loss': 1.17, 'grad_norm': 0.0007635309648150456, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:08<09:11, 3.65s/it] 71%|███████ | 370/520 [23:12<09:07, 3.65s/it] {'loss': 1.1229, 'grad_norm': 0.0008548854046378391, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:12<09:07, 3.65s/it] 71%|███████▏ | 371/520 [23:15<09:03, 3.64s/it] {'loss': 1.1207, 'grad_norm': 0.0009167438091047531, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:15<09:03, 3.64s/it] 72%|███████▏ | 372/520 [23:19<08:59, 3.64s/it] {'loss': 1.2419, 'grad_norm': 0.0008862082346399996, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:19<08:59, 3.64s/it] 72%|███████▏ | 373/520 [23:23<08:56, 3.65s/it] {'loss': 1.135, 'grad_norm': 0.0009198107021035284, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:23<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:26<08:52, 3.65s/it] {'loss': 1.2092, 'grad_norm': 0.000879514233916299, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:26<08:52, 3.65s/it] 72%|███████▏ | 375/520 [23:30<08:48, 3.65s/it] {'loss': 1.1283, 'grad_norm': 0.0009024901339904461, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:30<08:48, 3.65s/it] 72%|███████▏ | 376/520 [23:34<08:46, 3.66s/it] {'loss': 1.2337, 'grad_norm': 0.0008391531853267434, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:34<08:46, 3.66s/it] 72%|███████▎ | 377/520 [23:37<08:47, 3.69s/it] {'loss': 1.165, 'grad_norm': 0.0008914749691090647, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:37<08:47, 3.69s/it] 73%|███████▎ | 378/520 [23:41<08:43, 3.68s/it] {'loss': 1.2293, 'grad_norm': 0.0008467702453453944, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:41<08:43, 3.68s/it] 73%|███████▎ | 379/520 [23:45<08:40, 3.69s/it] {'loss': 1.2021, 'grad_norm': 0.0008433286828386039, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:45<08:40, 3.69s/it] 73%|███████▎ | 380/520 [23:48<08:33, 3.67s/it] {'loss': 1.2181, 'grad_norm': 0.0008731068225829498, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:48<08:33, 3.67s/it] 73%|███████▎ | 381/520 [23:52<08:30, 3.67s/it] {'loss': 1.207, 'grad_norm': 0.0008456321144336496, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:52<08:30, 3.67s/it] 73%|███████▎ | 382/520 [23:56<08:26, 3.67s/it] {'loss': 1.1868, 'grad_norm': 0.0008471413945098041, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:56<08:26, 3.67s/it] 74%|███████▎ | 383/520 [23:59<08:22, 3.67s/it] {'loss': 1.0484, 'grad_norm': 0.0009651381975727473, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:59<08:22, 3.67s/it] 74%|███████▍ | 384/520 [24:03<08:17, 3.65s/it] {'loss': 1.2223, 'grad_norm': 0.0008539476464418774, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:03<08:17, 3.65s/it] 74%|███████▍ | 385/520 [24:07<08:13, 3.65s/it] {'loss': 1.1884, 'grad_norm': 0.0008060422235565888, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:07<08:13, 3.65s/it] 74%|███████▍ | 386/520 [24:10<08:09, 3.65s/it] {'loss': 1.1414, 'grad_norm': 0.0007933655569917613, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:10<08:09, 3.65s/it] 74%|███████▍ | 387/520 [24:14<08:03, 3.64s/it] {'loss': 1.2421, 'grad_norm': 0.000897469505588036, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:14<08:03, 3.64s/it] 75%|███████▍ | 388/520 [24:18<07:59, 3.64s/it] {'loss': 1.0966, 'grad_norm': 0.0008594436227939199, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:18<07:59, 3.64s/it] 75%|███████▍ | 389/520 [24:21<07:55, 3.63s/it] {'loss': 1.144, 'grad_norm': 0.000986577870326608, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:21<07:55, 3.63s/it] 75%|███████▌ | 390/520 [24:25<07:51, 3.63s/it] {'loss': 1.2076, 'grad_norm': 0.0008539159771087478, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:25<07:51, 3.63s/it] 75%|███████▌ | 391/520 [24:28<07:49, 3.64s/it] {'loss': 1.2758, 'grad_norm': 0.0009672907052757885, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:28<07:49, 3.64s/it] 75%|███████▌ | 392/520 [24:32<07:47, 3.65s/it] {'loss': 1.1017, 'grad_norm': 0.0009007039234754292, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:32<07:47, 3.65s/it] 76%|███████▌ | 393/520 [24:36<07:43, 3.65s/it] {'loss': 1.0928, 'grad_norm': 0.0007182907695814453, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:36<07:43, 3.65s/it] 76%|███████▌ | 394/520 [24:39<07:40, 3.65s/it] {'loss': 1.1662, 'grad_norm': 0.0009209609711068428, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:39<07:40, 3.65s/it] 76%|███████▌ | 395/520 [24:43<07:36, 3.65s/it] {'loss': 1.129, 'grad_norm': 0.0009440394806002088, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:43<07:36, 3.65s/it] 76%|███████▌ | 396/520 [24:47<07:32, 3.65s/it] {'loss': 1.208, 'grad_norm': 0.0009260614965449402, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:47<07:32, 3.65s/it] 76%|███████▋ | 397/520 [24:50<07:29, 3.65s/it] {'loss': 1.1868, 'grad_norm': 0.0008307885527014618, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:50<07:29, 3.65s/it] 77%|███████▋ | 398/520 [24:54<07:24, 3.65s/it] {'loss': 1.1869, 'grad_norm': 0.0009120106263847169, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:54<07:24, 3.65s/it] 77%|███████▋ | 399/520 [24:58<07:21, 3.65s/it] {'loss': 1.1333, 'grad_norm': 0.0008308684659452222, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:58<07:21, 3.65s/it] 77%|███████▋ | 400/520 [25:01<07:17, 3.65s/it] {'loss': 1.162, 'grad_norm': 0.0008064960223236012, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:01<07:17, 3.65s/it] 77%|███████▋ | 401/520 [25:05<07:14, 3.65s/it] {'loss': 1.0231, 'grad_norm': 0.0009631189956104849, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:05<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:09<07:09, 3.64s/it] {'loss': 1.1476, 'grad_norm': 0.0009010723108678332, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:09<07:09, 3.64s/it] 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] {'loss': 1.1706, 'grad_norm': 0.0009503940689819357, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] 78%|███████▊ | 404/520 [25:16<07:01, 3.63s/it] {'loss': 1.0822, 'grad_norm': 0.0010111096615543177, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:16<07:01, 3.63s/it] 78%|███████▊ | 405/520 [25:19<06:56, 3.62s/it] {'loss': 1.1447, 'grad_norm': 0.0008917145342146562, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:19<06:56, 3.62s/it] 78%|███████▊ | 406/520 [25:23<06:53, 3.63s/it] {'loss': 1.0613, 'grad_norm': 0.0012406034157812936, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:23<06:53, 3.63s/it] 78%|███████▊ | 407/520 [25:27<06:49, 3.62s/it] {'loss': 1.2469, 'grad_norm': 0.0009604764737514689, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:27<06:49, 3.62s/it] 78%|███████▊ | 408/520 [25:30<06:44, 3.61s/it] {'loss': 1.1634, 'grad_norm': 0.0011572158627555372, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:30<06:44, 3.61s/it] 79%|███████▊ | 409/520 [25:34<06:45, 3.65s/it] {'loss': 1.2739, 'grad_norm': 0.0009524531495331462, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:34<06:45, 3.65s/it] 79%|███████▉ | 410/520 [25:38<06:48, 3.71s/it] {'loss': 1.018, 'grad_norm': 0.0009203161378105548, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:38<06:48, 3.71s/it] 79%|███████▉ | 411/520 [25:42<06:47, 3.74s/it] {'loss': 1.258, 'grad_norm': 0.0009935544592892265, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:42<06:47, 3.74s/it] 79%|███████▉ | 412/520 [25:46<06:47, 3.77s/it] {'loss': 1.1675, 'grad_norm': 0.0008823767064075077, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:46<06:47, 3.77s/it] 79%|███████▉ | 413/520 [25:49<06:44, 3.78s/it] {'loss': 1.1544, 'grad_norm': 0.0008252357737413232, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:49<06:44, 3.78s/it] 80%|███████▉ | 414/520 [25:53<06:41, 3.79s/it] {'loss': 0.9713, 'grad_norm': 0.000727703936135161, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:53<06:41, 3.79s/it] 80%|███████▉ | 415/520 [25:57<06:38, 3.79s/it] {'loss': 1.1483, 'grad_norm': 0.0008195598201453246, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:57<06:38, 3.79s/it] 80%|████████ | 416/520 [26:01<06:34, 3.79s/it] {'loss': 1.0613, 'grad_norm': 0.0009387583914888887, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:01<06:34, 3.79s/it] 80%|████████ | 417/520 [26:04<06:30, 3.79s/it] {'loss': 1.2195, 'grad_norm': 0.0009362031372045727, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:05<06:30, 3.79s/it] 80%|████████ | 418/520 [26:08<06:27, 3.80s/it] {'loss': 1.2105, 'grad_norm': 0.0009166098703535079, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:08<06:27, 3.80s/it] 81%|████████ | 419/520 [26:12<06:22, 3.79s/it] {'loss': 1.2044, 'grad_norm': 0.0009360135485092542, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:12<06:22, 3.79s/it] 81%|████████ | 420/520 [26:16<06:18, 3.79s/it] {'loss': 1.098, 'grad_norm': 0.0009453973071532573, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:16<06:18, 3.79s/it] 81%|████████ | 421/520 [26:20<06:15, 3.79s/it] {'loss': 1.0345, 'grad_norm': 0.0010577879212100212, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:20<06:15, 3.79s/it] 81%|████████ | 422/520 [26:23<06:12, 3.80s/it] {'loss': 1.1515, 'grad_norm': 0.0008836756341493949, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:23<06:12, 3.80s/it] 81%|████████▏ | 423/520 [26:27<06:08, 3.80s/it] {'loss': 1.1306, 'grad_norm': 0.000999722422182873, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:27<06:08, 3.80s/it] 82%|████████▏ | 424/520 [26:31<06:02, 3.78s/it] {'loss': 1.2395, 'grad_norm': 0.0008923296938776805, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:31<06:02, 3.78s/it] 82%|████████▏ | 425/520 [26:35<05:57, 3.77s/it] {'loss': 1.1449, 'grad_norm': 0.0008375132921617557, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:35<05:57, 3.77s/it] 82%|████████▏ | 426/520 [26:39<05:54, 3.77s/it] {'loss': 1.1652, 'grad_norm': 0.0011228315997121481, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:39<05:54, 3.77s/it] 82%|████████▏ | 427/520 [26:42<05:51, 3.78s/it] {'loss': 1.0777, 'grad_norm': 0.0008338591255939994, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:42<05:51, 3.78s/it] 82%|████████▏ | 428/520 [26:46<05:48, 3.78s/it] {'loss': 1.0637, 'grad_norm': 0.0009292800575276456, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:46<05:48, 3.78s/it] 82%|████████▎ | 429/520 [26:50<05:44, 3.78s/it] {'loss': 1.155, 'grad_norm': 0.0008893470503693866, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:50<05:44, 3.78s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:54<05:42, 3.80s/it] {'loss': 1.1593, 'grad_norm': 0.0008077002614922745, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:54<05:42, 3.80s/it] 83%|████████▎ | 431/520 [26:58<05:38, 3.80s/it] {'loss': 1.1261, 'grad_norm': 0.000919626217882441, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:58<05:38, 3.80s/it] 83%|████████▎ | 432/520 [27:01<05:34, 3.80s/it] {'loss': 1.0694, 'grad_norm': 0.0008981152846156195, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:01<05:34, 3.80s/it] 83%|████████▎ | 433/520 [27:05<05:30, 3.79s/it] {'loss': 1.1999, 'grad_norm': 0.000890768039028651, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:05<05:30, 3.79s/it] 83%|████████▎ | 434/520 [27:09<05:26, 3.79s/it] {'loss': 0.952, 'grad_norm': 0.0008542042451189286, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:09<05:26, 3.79s/it] 84%|████████▎ | 435/520 [27:13<05:21, 3.79s/it] {'loss': 1.2322, 'grad_norm': 0.0009765646517224023, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:13<05:21, 3.79s/it] 84%|████████▍ | 436/520 [27:16<05:17, 3.78s/it] {'loss': 1.0409, 'grad_norm': 0.0008967758149167239, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:16<05:17, 3.78s/it] 84%|████████▍ | 437/520 [27:20<05:13, 3.78s/it] {'loss': 1.255, 'grad_norm': 0.0008988583158138992, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:20<05:13, 3.78s/it] 84%|████████▍ | 438/520 [27:24<05:09, 3.78s/it] {'loss': 1.0792, 'grad_norm': 0.0008887205510196545, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:24<05:09, 3.78s/it] 84%|████████▍ | 439/520 [27:28<05:05, 3.77s/it] {'loss': 1.1103, 'grad_norm': 0.0007270017220392174, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:28<05:05, 3.77s/it] 85%|████████▍ | 440/520 [27:32<05:00, 3.76s/it] {'loss': 1.108, 'grad_norm': 0.0008772713282203918, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:32<05:00, 3.76s/it] 85%|████████▍ | 441/520 [27:35<04:57, 3.76s/it] {'loss': 1.1248, 'grad_norm': 0.0009397827297582472, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:35<04:57, 3.76s/it] 85%|████████▌ | 442/520 [27:39<04:52, 3.76s/it] {'loss': 1.1741, 'grad_norm': 0.0009744638385068858, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:39<04:52, 3.76s/it] 85%|████████▌ | 443/520 [27:43<04:49, 3.76s/it] {'loss': 1.186, 'grad_norm': 0.0008682363561982912, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:43<04:49, 3.76s/it] 85%|████████▌ | 444/520 [27:47<04:45, 3.75s/it] {'loss': 1.1508, 'grad_norm': 0.0007994844303556491, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:47<04:45, 3.75s/it] 86%|████████▌ | 445/520 [27:50<04:41, 3.75s/it] {'loss': 1.0785, 'grad_norm': 0.0008672909106455859, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:50<04:41, 3.75s/it] 86%|████████▌ | 446/520 [27:54<04:37, 3.75s/it] {'loss': 1.2013, 'grad_norm': 0.0007994326107302206, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:54<04:37, 3.75s/it] 86%|████████▌ | 447/520 [27:58<04:34, 3.76s/it] {'loss': 1.1528, 'grad_norm': 0.0008538677787393131, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:58<04:34, 3.76s/it] 86%|████████▌ | 448/520 [28:02<04:30, 3.75s/it] {'loss': 1.1492, 'grad_norm': 0.0009051219277136851, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [28:02<04:30, 3.75s/it] 86%|████████▋ | 449/520 [28:05<04:26, 3.76s/it] {'loss': 1.1612, 'grad_norm': 0.0008879638846358994, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:05<04:26, 3.76s/it] 87%|████████▋ | 450/520 [28:09<04:22, 3.75s/it] {'loss': 1.1746, 'grad_norm': 0.0008688241890047253, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:09<04:22, 3.75s/it] 87%|████████▋ | 451/520 [28:13<04:19, 3.76s/it] {'loss': 1.1753, 'grad_norm': 0.0008916485392813639, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:13<04:19, 3.76s/it] 87%|████████▋ | 452/520 [28:16<04:13, 3.73s/it] {'loss': 1.2039, 'grad_norm': 0.0008018553232135694, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:16<04:13, 3.73s/it] 87%|████████▋ | 453/520 [28:20<04:07, 3.69s/it] {'loss': 1.1782, 'grad_norm': 0.0008750112885176588, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:20<04:07, 3.69s/it] 87%|████████▋ | 454/520 [28:24<04:03, 3.69s/it] {'loss': 1.0864, 'grad_norm': 0.000898051933605236, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:24<04:03, 3.69s/it] 88%|████████▊ | 455/520 [28:27<03:58, 3.67s/it] {'loss': 1.2262, 'grad_norm': 0.0008657890674106066, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:27<03:58, 3.67s/it] 88%|████████▊ | 456/520 [28:31<03:54, 3.66s/it] {'loss': 1.1534, 'grad_norm': 0.0008804082975959664, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:31<03:54, 3.66s/it] 88%|████████▊ | 457/520 [28:35<03:50, 3.67s/it] {'loss': 1.0802, 'grad_norm': 0.0007731934949798186, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:35<03:50, 3.67s/it] 88%|████████▊ | 458/520 [28:38<03:49, 3.70s/it] {'loss': 1.2761, 'grad_norm': 0.0009791580262779123, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 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520/520 [32:35<00:00, 3.92s/it] 100%|██████████| 520/520 [32:35<00:00, 3.76s/it] +[2025-10-18 00:48:51,812] [INFO] [launch.py:348:main] Process 788727 exits successfully. +[2025-10-18 00:48:51,813] [INFO] [launch.py:348:main] Process 788729 exits successfully. +[2025-10-18 00:48:51,813] [INFO] [launch.py:348:main] Process 788728 exits successfully. +[2025-10-18 00:48:51,814] [INFO] [launch.py:348:main] Process 788731 exits successfully. +[2025-10-18 00:48:51,814] [INFO] [launch.py:348:main] Process 788733 exits successfully. +[2025-10-18 00:48:52,816] [INFO] [launch.py:348:main] Process 788730 exits successfully. +[2025-10-18 00:48:52,816] [INFO] [launch.py:348:main] Process 788732 exits successfully. +[2025-10-18 00:48:55,820] [INFO] [launch.py:348:main] Process 788726 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-7.0_2.9_2e-1_connector-7.0_2.9_2e-1_ablation_20251018_001445.log +Timestamp: 2025-10-18 00:48:58 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation_20251018_004858.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation_20251018_004858.log new file mode 100644 index 0000000000000000000000000000000000000000..8340b336993b2791d03a849812277953398f149b --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation_20251018_004858.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation_20251018_004858.log +Timestamp: 2025-10-18 00:48:58 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:49:01,061] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:04,235] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 00:49:04,237] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 1.1 --temperature_mlp_text 1.1 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 1.1 --temperature_mlp_vision 1.1 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 1.1 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:49:06,798] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:07,869] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 00:49:07,869] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 00:49:07,869] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 00:49:07,869] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 00:49:07,869] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 00:49:07,869] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 00:49:07,869] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 00:49:07,871] [INFO] [launch.py:253:main] process 810720 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,873] [INFO] [launch.py:253:main] process 810721 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,875] [INFO] [launch.py:253:main] process 810722 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,877] [INFO] [launch.py:253:main] process 810723 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,879] [INFO] [launch.py:253:main] process 810724 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,881] [INFO] [launch.py:253:main] process 810725 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,882] [INFO] [launch.py:253:main] process 810726 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 00:49:07,884] [INFO] [launch.py:253:main] process 810727 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.1', '--temperature_mlp_text', '1.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.1', '--temperature_mlp_vision', '1.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 00:49:14,825] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:14,836] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:14,898] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:14,972] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:14,975] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:15,002] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:15,007] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:15,021] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 00:49:15,380] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,380] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 00:49:15,380] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,380] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,386] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,387] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,415] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,424] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 00:49:15,434] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.1, 'temperature_mlp': 1.1, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.1, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.1, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.1, + "temperature_mlp": 1.1, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:810720:810720 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Using network Socket +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:810722:810722 [2] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:810727:810727 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:810727:810727 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:810727:810727 [7] 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0 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810725:812312 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:810723:812311 [3] NCCL INFO ncclCommInitRank comm 0x5606e6e843a0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x396b2b7264357f57 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810721:812315 [1] NCCL INFO ncclCommInitRank comm 0x560fdb1d1440 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x396b2b7264357f57 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810720:812294 [0] NCCL INFO ncclCommInitRank comm 0x5562d4012e70 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x396b2b7264357f57 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:810722:812316 [2] NCCL INFO ncclCommInitRank comm 0x560818ecea20 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x396b2b7264357f57 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810724:812313 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:810726:812314 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:810727:812317 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. 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[5] NCCL INFO ncclCommInitRank comm 0x562186745d50 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x396b2b7264357f57 - Init COMPLETE +[2025-10-18 00:50:01,819] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 00:50:03,537] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 00:50:21,529 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 00:50:21,535 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:006->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:810722:817366 [2] NCCL INFO ncclCommInitRank comm 0x7f0f8406ae30 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810721:817365 [1] NCCL INFO ncclCommInitRank comm 0x7f122c06acd0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810720:817361 [0] NCCL INFO ncclCommInitRank comm 0x7f6cac06aa40 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810726:817367 [6] NCCL INFO ncclCommInitRank comm 0x7f17f806aea0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810724:817362 [4] NCCL INFO ncclCommInitRank comm 0x7fa9f406b450 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810727:817368 [7] NCCL INFO ncclCommInitRank comm 0x7f12c406a240 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810725:817363 [5] NCCL INFO ncclCommInitRank comm 0x7fd4a406ba30 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x809850eb445c3f55 - Init COMPLETE +ywang29-vrdb-test1-worker-0:810723:817364 [3] NCCL INFO ncclCommInitRank comm 0x7f284806b080 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x809850eb445c3f55 - Init COMPLETE + 0%| | 1/520 [00:14<2:05:47, 14.54s/it] {'loss': 2.0497, 'grad_norm': 0.0, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:05:47, 14.54s/it] 0%| | 2/520 [00:18<1:10:03, 8.12s/it] {'loss': 2.06, 'grad_norm': 0.0, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:10:03, 8.12s/it] 1%| | 3/520 [00:21<52:32, 6.10s/it] {'loss': 2.1958, 'grad_norm': 0.0, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<52:32, 6.10s/it] 1%| | 4/520 [00:25<44:12, 5.14s/it] {'loss': 2.0688, 'grad_norm': 0.0, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<44:12, 5.14s/it] 1%| | 5/520 [00:29<39:30, 4.60s/it] {'loss': 2.2403, 'grad_norm': 0.0, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<39:30, 4.60s/it] 1%| | 6/520 [00:32<36:48, 4.30s/it] {'loss': 1.6782, 'grad_norm': 0.0, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:48, 4.30s/it] 1%|▏ | 7/520 [00:36<34:59, 4.09s/it] {'loss': 2.0829, 'grad_norm': 0.0, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<34:59, 4.09s/it] 2%|▏ | 8/520 [00:40<35:32, 4.16s/it] {'loss': 2.0585, 'grad_norm': 0.0, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<35:32, 4.16s/it] 2%|▏ | 9/520 [00:44<34:00, 3.99s/it] {'loss': 2.1936, 'grad_norm': 0.0, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<34:00, 3.99s/it] 2%|▏ | 10/520 [00:48<32:55, 3.87s/it] {'loss': 2.0887, 'grad_norm': 0.0, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<32:55, 3.87s/it] 2%|▏ | 11/520 [00:51<32:29, 3.83s/it] {'loss': 2.0637, 'grad_norm': 0.0, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:51<32:29, 3.83s/it] 2%|▏ | 12/520 [00:55<31:49, 3.76s/it] {'loss': 1.8848, 'grad_norm': 0.0, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<31:49, 3.76s/it][2025-10-18 00:51:26,208] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:59<33:09, 3.93s/it] {'loss': 2.0728, 'grad_norm': 0.0, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:59<33:09, 3.93s/it] 3%|▎ | 14/520 [01:03<32:14, 3.82s/it] {'loss': 2.1118, 'grad_norm': 0.0, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:03<32:14, 3.82s/it] 3%|▎ | 15/520 [01:06<31:41, 3.77s/it] {'loss': 1.7478, 'grad_norm': 0.0, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:06<31:41, 3.77s/it] 3%|▎ | 16/520 [01:10<31:32, 3.75s/it] {'loss': 1.8954, 'grad_norm': 0.0, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:10<31:32, 3.75s/it] 3%|▎ | 17/520 [01:14<31:25, 3.75s/it] {'loss': 2.1158, 'grad_norm': 0.0, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:14<31:25, 3.75s/it] 3%|▎ | 18/520 [01:18<31:17, 3.74s/it] {'loss': 2.1718, 'grad_norm': 0.0, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:18<31:17, 3.74s/it] 4%|▎ | 19/520 [01:21<31:17, 3.75s/it] {'loss': 1.8467, 'grad_norm': 0.0, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:21<31:17, 3.75s/it] 4%|▍ | 20/520 [01:25<31:16, 3.75s/it] {'loss': 2.2091, 'grad_norm': 0.0, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:25<31:16, 3.75s/it] 4%|▍ | 21/520 [01:29<31:12, 3.75s/it] {'loss': 2.0718, 'grad_norm': 0.0, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:29<31:12, 3.75s/it] 4%|▍ | 22/520 [01:33<31:09, 3.75s/it] {'loss': 2.0488, 'grad_norm': 0.0, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:33<31:09, 3.75s/it] 4%|▍ | 23/520 [01:36<31:04, 3.75s/it] {'loss': 2.0811, 'grad_norm': 0.0, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:36<31:04, 3.75s/it] 5%|▍ | 24/520 [01:40<31:03, 3.76s/it] {'loss': 1.8639, 'grad_norm': 0.0, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<31:03, 3.76s/it] 5%|▍ | 25/520 [01:44<30:57, 3.75s/it] {'loss': 2.2763, 'grad_norm': 0.0, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:44<30:57, 3.75s/it] 5%|▌ | 26/520 [01:48<30:57, 3.76s/it] {'loss': 1.9761, 'grad_norm': 0.0, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<30:57, 3.76s/it] 5%|▌ | 27/520 [01:51<30:48, 3.75s/it] {'loss': 2.055, 'grad_norm': 0.0, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:51<30:48, 3.75s/it] 5%|▌ | 28/520 [01:55<30:48, 3.76s/it] {'loss': 2.2197, 'grad_norm': 0.0, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:55<30:48, 3.76s/it] 6%|▌ | 29/520 [01:59<30:45, 3.76s/it] {'loss': 2.145, 'grad_norm': 0.0, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:59<30:45, 3.76s/it] 6%|▌ | 30/520 [02:03<30:40, 3.76s/it] {'loss': 1.9357, 'grad_norm': 0.0, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:03<30:40, 3.76s/it] 6%|▌ | 31/520 [02:06<30:39, 3.76s/it] {'loss': 1.9528, 'grad_norm': 0.0, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:07<30:39, 3.76s/it] 6%|▌ | 32/520 [02:10<30:33, 3.76s/it] {'loss': 1.6773, 'grad_norm': 0.0, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:10<30:33, 3.76s/it] 6%|▋ | 33/520 [02:14<30:28, 3.75s/it] {'loss': 2.148, 'grad_norm': 0.0, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:14<30:28, 3.75s/it] 7%|▋ | 34/520 [02:18<30:25, 3.76s/it] {'loss': 2.2337, 'grad_norm': 0.0, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:18<30:25, 3.76s/it] 7%|▋ | 35/520 [02:22<30:20, 3.75s/it] {'loss': 2.1437, 'grad_norm': 0.0, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:22<30:20, 3.75s/it] 7%|▋ | 36/520 [02:25<30:18, 3.76s/it] {'loss': 2.0502, 'grad_norm': 0.0, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:25<30:18, 3.76s/it] 7%|▋ | 37/520 [02:29<30:16, 3.76s/it] {'loss': 1.911, 'grad_norm': 0.0, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:29<30:16, 3.76s/it] 7%|▋ | 38/520 [02:33<30:14, 3.76s/it] {'loss': 2.0439, 'grad_norm': 0.0, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:33<30:14, 3.76s/it] 8%|▊ | 39/520 [02:37<30:08, 3.76s/it] {'loss': 2.2716, 'grad_norm': 0.0, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:37<30:08, 3.76s/it] 8%|▊ | 40/520 [02:40<30:03, 3.76s/it] {'loss': 2.0359, 'grad_norm': 0.0, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:40<30:03, 3.76s/it] 8%|▊ | 41/520 [02:44<29:59, 3.76s/it] {'loss': 2.1421, 'grad_norm': 0.0, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:44<29:59, 3.76s/it] 8%|▊ | 42/520 [02:48<29:53, 3.75s/it] {'loss': 2.3152, 'grad_norm': 0.0, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:48<29:53, 3.75s/it] 8%|▊ | 43/520 [02:52<29:49, 3.75s/it] {'loss': 1.9193, 'grad_norm': 0.0, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:52<29:49, 3.75s/it] 8%|▊ | 44/520 [02:55<29:45, 3.75s/it] {'loss': 1.9613, 'grad_norm': 0.0, 'learning_rate': 0.19848077530122082, 'epoch': 0.08} + 8%|▊ | 44/520 [02:55<29:45, 3.75s/it] 9%|▊ | 45/520 [02:59<29:43, 3.76s/it] {'loss': 2.2185, 'grad_norm': 0.0, 'learning_rate': 0.19837062236509015, 'epoch': 0.09} + 9%|▊ | 45/520 [02:59<29:43, 3.76s/it] 9%|▉ | 46/520 [03:03<29:28, 3.73s/it] {'loss': 1.9333, 'grad_norm': 0.0, 'learning_rate': 0.19825664732332884, 'epoch': 0.09} + 9%|▉ | 46/520 [03:03<29:28, 3.73s/it] 9%|▉ | 47/520 [03:06<29:07, 3.69s/it] {'loss': 2.1175, 'grad_norm': 0.0, 'learning_rate': 0.19813885460433878, 'epoch': 0.09} + 9%|▉ | 47/520 [03:06<29:07, 3.69s/it] 9%|▉ | 48/520 [03:10<28:52, 3.67s/it] {'loss': 2.2907, 'grad_norm': 0.0, 'learning_rate': 0.19801724878485438, 'epoch': 0.09} + 9%|▉ | 48/520 [03:10<28:52, 3.67s/it] 9%|▉ | 49/520 [03:14<28:37, 3.65s/it] {'loss': 2.1249, 'grad_norm': 0.0, 'learning_rate': 0.19789183458976486, 'epoch': 0.09} + 9%|▉ | 49/520 [03:14<28:37, 3.65s/it] 10%|▉ | 50/520 [03:17<28:29, 3.64s/it] {'loss': 2.1602, 'grad_norm': 0.0, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:17<28:29, 3.64s/it] 10%|▉ | 51/520 [03:21<28:18, 3.62s/it] {'loss': 2.2575, 'grad_norm': 0.0, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:21<28:18, 3.62s/it] 10%|█ | 52/520 [03:24<28:20, 3.63s/it] {'loss': 2.2524, 'grad_norm': 0.0, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:24<28:20, 3.63s/it] 10%|█ | 53/520 [03:28<28:42, 3.69s/it] {'loss': 2.0526, 'grad_norm': 0.0, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:28<28:42, 3.69s/it] 10%|█ | 54/520 [03:32<28:58, 3.73s/it] {'loss': 2.1465, 'grad_norm': 0.0, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:32<28:58, 3.73s/it] 11%|█ | 55/520 [03:36<28:58, 3.74s/it] {'loss': 2.2296, 'grad_norm': 0.0, 'learning_rate': 0.1970596567453391, 'epoch': 0.11} + 11%|█ | 55/520 [03:36<28:58, 3.74s/it] 11%|█ | 56/520 [03:39<28:35, 3.70s/it] {'loss': 2.1652, 'grad_norm': 0.0, 'learning_rate': 0.1969077286229078, 'epoch': 0.11} + 11%|█ | 56/520 [03:39<28:35, 3.70s/it] 11%|█ | 57/520 [03:43<28:17, 3.67s/it] {'loss': 2.1812, 'grad_norm': 0.0, 'learning_rate': 0.19675203523431964, 'epoch': 0.11} + 11%|█ | 57/520 [03:43<28:17, 3.67s/it] 11%|█ | 58/520 [03:47<28:03, 3.64s/it] {'loss': 1.9834, 'grad_norm': 0.0, 'learning_rate': 0.19659258262890683, 'epoch': 0.11} + 11%|█ | 58/520 [03:47<28:03, 3.64s/it] 11%|█▏ | 59/520 [03:50<27:55, 3.63s/it] {'loss': 1.8767, 'grad_norm': 0.0, 'learning_rate': 0.19642937700206278, 'epoch': 0.11} + 11%|█▏ | 59/520 [03:50<27:55, 3.63s/it] 12%|█▏ | 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3.81s/it] 57%|█████▋ | 294/520 [18:12<14:21, 3.81s/it] {'loss': 2.2303, 'grad_norm': 0.0, 'learning_rate': 0.08386422393671933, 'epoch': 0.57} + 57%|█████▋ | 294/520 [18:12<14:21, 3.81s/it] 57%|█████▋ | 295/520 [18:16<14:20, 3.82s/it] {'loss': 1.832, 'grad_norm': 0.0, 'learning_rate': 0.08324937766952638, 'epoch': 0.57} + 57%|█████▋ | 295/520 [18:16<14:20, 3.82s/it] 57%|█████▋ | 296/520 [18:20<14:16, 3.82s/it] {'loss': 2.0787, 'grad_norm': 0.0, 'learning_rate': 0.08263518223330697, 'epoch': 0.57} + 57%|█████▋ | 296/520 [18:20<14:16, 3.82s/it] 57%|█████▋ | 297/520 [18:23<14:11, 3.82s/it] {'loss': 2.1771, 'grad_norm': 0.0, 'learning_rate': 0.08202166149209474, 'epoch': 0.57} + 57%|█████▋ | 297/520 [18:23<14:11, 3.82s/it] 57%|█████▋ | 298/520 [18:27<14:05, 3.81s/it] {'loss': 1.9442, 'grad_norm': 0.0, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:27<14:05, 3.81s/it] 57%|█████▊ | 299/520 [18:31<14:00, 3.80s/it] {'loss': 1.8794, 'grad_norm': 0.0, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:31<14:00, 3.80s/it] 58%|█████▊ | 300/520 [18:35<13:56, 3.80s/it] {'loss': 2.1153, 'grad_norm': 0.0, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:35<13:56, 3.80s/it] 58%|█████▊ | 301/520 [18:39<13:52, 3.80s/it] {'loss': 2.0707, 'grad_norm': 0.0, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:39<13:52, 3.80s/it] 58%|█████▊ | 302/520 [18:42<13:48, 3.80s/it] {'loss': 1.8532, 'grad_norm': 0.0, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:42<13:48, 3.80s/it] 58%|█████▊ | 303/520 [18:46<13:44, 3.80s/it] {'loss': 2.1844, 'grad_norm': 0.0, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:46<13:44, 3.80s/it] 58%|█████▊ | 304/520 [18:50<14:04, 3.91s/it] {'loss': 2.081, 'grad_norm': 0.0, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [18:50<14:04, 3.91s/it] 59%|█████▊ | 305/520 [18:54<13:52, 3.87s/it] {'loss': 2.1651, 'grad_norm': 0.0, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [18:54<13:52, 3.87s/it] 59%|█████▉ | 306/520 [18:58<13:45, 3.86s/it] {'loss': 2.1107, 'grad_norm': 0.0, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [18:58<13:45, 3.86s/it] 59%|█████▉ | 307/520 [19:02<13:35, 3.83s/it] {'loss': 2.0262, 'grad_norm': 0.0, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:02<13:35, 3.83s/it] 59%|█████▉ | 308/520 [19:05<13:16, 3.76s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:05<13:16, 3.76s/it] 59%|█████▉ | 309/520 [19:09<13:05, 3.72s/it] {'loss': 1.9269, 'grad_norm': 0.0, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:09<13:05, 3.72s/it] 60%|█████▉ | 310/520 [19:13<12:53, 3.68s/it] {'loss': 1.9954, 'grad_norm': 0.0, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:13<12:53, 3.68s/it] 60%|█████▉ | 311/520 [19:16<12:45, 3.66s/it] {'loss': 2.065, 'grad_norm': 0.0, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:16<12:45, 3.66s/it] 60%|██████ | 312/520 [19:20<12:39, 3.65s/it] {'loss': 2.1635, 'grad_norm': 0.0, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:20<12:39, 3.65s/it] 60%|██████ | 313/520 [19:23<12:35, 3.65s/it] {'loss': 1.8959, 'grad_norm': 0.0, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:23<12:35, 3.65s/it] 60%|██████ | 314/520 [19:27<12:57, 3.77s/it] {'loss': 2.0684, 'grad_norm': 0.0, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:27<12:57, 3.77s/it] 61%|██████ | 315/520 [19:31<12:43, 3.72s/it] {'loss': 2.0994, 'grad_norm': 0.0, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:31<12:43, 3.72s/it] 61%|██████ | 316/520 [19:35<12:55, 3.80s/it] {'loss': 2.1863, 'grad_norm': 0.0, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:35<12:55, 3.80s/it] 61%|██████ | 317/520 [19:39<12:42, 3.76s/it] {'loss': 1.9533, 'grad_norm': 0.0, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:39<12:42, 3.76s/it] 61%|██████ | 318/520 [19:42<12:30, 3.71s/it] {'loss': 2.2686, 'grad_norm': 0.0, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:42<12:30, 3.71s/it] 61%|██████▏ | 319/520 [19:46<12:43, 3.80s/it] {'loss': 1.8863, 'grad_norm': 0.0, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:46<12:43, 3.80s/it] 62%|██████▏ | 320/520 [19:50<12:27, 3.74s/it] {'loss': 2.0865, 'grad_norm': 0.0, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:50<12:27, 3.74s/it] 62%|██████▏ | 321/520 [19:54<12:17, 3.71s/it] {'loss': 2.0712, 'grad_norm': 0.0, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [19:54<12:17, 3.71s/it] 62%|██████▏ | 322/520 [19:57<12:09, 3.68s/it] {'loss': 1.8969, 'grad_norm': 0.0, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [19:57<12:09, 3.68s/it] 62%|██████▏ | 323/520 [20:01<12:00, 3.66s/it] {'loss': 2.0202, 'grad_norm': 0.0, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:01<12:00, 3.66s/it] 62%|██████▏ | 324/520 [20:04<11:54, 3.64s/it] {'loss': 2.0551, 'grad_norm': 0.0, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:04<11:54, 3.64s/it] 62%|██████▎ | 325/520 [20:08<11:49, 3.64s/it] {'loss': 2.1566, 'grad_norm': 0.0, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:08<11:49, 3.64s/it] 63%|██████▎ | 326/520 [20:12<11:43, 3.63s/it] {'loss': 2.1909, 'grad_norm': 0.0, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:12<11:43, 3.63s/it] 63%|██████▎ | 327/520 [20:15<11:38, 3.62s/it] {'loss': 2.061, 'grad_norm': 0.0, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:15<11:38, 3.62s/it] 63%|██████▎ | 328/520 [20:19<11:35, 3.62s/it] {'loss': 2.1111, 'grad_norm': 0.0, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:19<11:35, 3.62s/it] 63%|██████▎ | 329/520 [20:22<11:32, 3.62s/it] {'loss': 1.9436, 'grad_norm': 0.0, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:22<11:32, 3.62s/it] 63%|██████▎ | 330/520 [20:26<11:31, 3.64s/it] {'loss': 2.1281, 'grad_norm': 0.0, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:26<11:31, 3.64s/it] 64%|██████▎ | 331/520 [20:30<11:29, 3.65s/it] {'loss': 2.1551, 'grad_norm': 0.0, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:30<11:29, 3.65s/it] 64%|██████▍ | 332/520 [20:33<11:26, 3.65s/it] {'loss': 1.8491, 'grad_norm': 0.0, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:33<11:26, 3.65s/it] 64%|██████▍ | 333/520 [20:37<11:24, 3.66s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:37<11:24, 3.66s/it] 64%|██████▍ | 334/520 [20:41<11:19, 3.65s/it] {'loss': 2.1157, 'grad_norm': 0.0, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:41<11:19, 3.65s/it] 64%|██████▍ | 335/520 [20:44<11:15, 3.65s/it] {'loss': 2.013, 'grad_norm': 0.0, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:44<11:15, 3.65s/it] 65%|██████▍ | 336/520 [20:48<11:12, 3.66s/it] {'loss': 2.1874, 'grad_norm': 0.0, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:48<11:12, 3.66s/it] 65%|██████▍ | 337/520 [20:52<11:09, 3.66s/it] {'loss': 2.2477, 'grad_norm': 0.0, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:52<11:09, 3.66s/it] 65%|██████▌ | 338/520 [20:55<11:05, 3.66s/it] {'loss': 2.1774, 'grad_norm': 0.0, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [20:55<11:05, 3.66s/it] 65%|██████▌ | 339/520 [20:59<11:02, 3.66s/it] {'loss': 2.126, 'grad_norm': 0.0, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [20:59<11:02, 3.66s/it] 65%|██████▌ | 340/520 [21:03<11:00, 3.67s/it] {'loss': 2.0845, 'grad_norm': 0.0, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:03<11:00, 3.67s/it] 66%|██████▌ | 341/520 [21:06<10:57, 3.67s/it] {'loss': 2.094, 'grad_norm': 0.0, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:06<10:57, 3.67s/it] 66%|██████▌ | 342/520 [21:10<10:52, 3.66s/it] {'loss': 2.0199, 'grad_norm': 0.0, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:10<10:52, 3.66s/it] 66%|██████▌ | 343/520 [21:14<10:48, 3.66s/it] {'loss': 1.7182, 'grad_norm': 0.0, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:14<10:48, 3.66s/it] 66%|██████▌ | 344/520 [21:17<10:45, 3.67s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:17<10:45, 3.67s/it] 66%|██████▋ | 345/520 [21:21<10:42, 3.67s/it] {'loss': 2.2588, 'grad_norm': 0.0, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:21<10:42, 3.67s/it] 67%|██████▋ | 346/520 [21:25<10:38, 3.67s/it] {'loss': 1.859, 'grad_norm': 0.0, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:25<10:38, 3.67s/it] 67%|██████▋ | 347/520 [21:29<10:37, 3.69s/it] {'loss': 1.9277, 'grad_norm': 0.0, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:29<10:37, 3.69s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:32<10:33, 3.68s/it] {'loss': 2.405, 'grad_norm': 0.0, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:32<10:33, 3.68s/it] 67%|██████▋ | 349/520 [21:36<10:27, 3.67s/it] {'loss': 2.2236, 'grad_norm': 0.0, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:36<10:27, 3.67s/it] 67%|██████▋ | 350/520 [21:40<10:24, 3.67s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:40<10:24, 3.67s/it] 68%|██████▊ | 351/520 [21:43<10:20, 3.67s/it] {'loss': 2.0414, 'grad_norm': 0.0, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:43<10:20, 3.67s/it] 68%|██████▊ | 352/520 [21:47<10:15, 3.66s/it] {'loss': 2.0824, 'grad_norm': 0.0, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:47<10:15, 3.66s/it] 68%|██████▊ | 353/520 [21:51<10:11, 3.66s/it] {'loss': 1.8115, 'grad_norm': 0.0, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:51<10:11, 3.66s/it] 68%|██████▊ | 354/520 [21:54<10:06, 3.65s/it] {'loss': 1.8797, 'grad_norm': 0.0, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:54<10:06, 3.65s/it] 68%|██████▊ | 355/520 [21:58<10:00, 3.64s/it] {'loss': 2.0561, 'grad_norm': 0.0, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [21:58<10:00, 3.64s/it] 68%|██████▊ | 356/520 [22:01<09:55, 3.63s/it] {'loss': 2.2531, 'grad_norm': 0.0, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:01<09:55, 3.63s/it] 69%|██████▊ | 357/520 [22:05<09:50, 3.63s/it] {'loss': 2.0294, 'grad_norm': 0.0, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:05<09:50, 3.63s/it] 69%|██████▉ | 358/520 [22:09<09:49, 3.64s/it] {'loss': 2.0531, 'grad_norm': 0.0, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:09<09:49, 3.64s/it] 69%|██████▉ | 359/520 [22:12<09:44, 3.63s/it] {'loss': 2.011, 'grad_norm': 0.0, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:12<09:44, 3.63s/it] 69%|██████▉ | 360/520 [22:16<09:40, 3.63s/it] {'loss': 1.9948, 'grad_norm': 0.0, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:16<09:40, 3.63s/it] 69%|██████▉ | 361/520 [22:20<09:37, 3.63s/it] {'loss': 1.7504, 'grad_norm': 0.0, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:20<09:37, 3.63s/it] 70%|██████▉ | 362/520 [22:23<09:33, 3.63s/it] {'loss': 2.2058, 'grad_norm': 0.0, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:23<09:33, 3.63s/it] 70%|██████▉ | 363/520 [22:27<09:34, 3.66s/it] {'loss': 2.0753, 'grad_norm': 0.0, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:27<09:34, 3.66s/it] 70%|███████ | 364/520 [22:31<09:31, 3.67s/it] {'loss': 1.9811, 'grad_norm': 0.0, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:31<09:31, 3.67s/it] 70%|███████ | 365/520 [22:34<09:26, 3.66s/it] {'loss': 2.1137, 'grad_norm': 0.0, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:34<09:26, 3.66s/it] 70%|███████ | 366/520 [22:38<09:24, 3.67s/it] {'loss': 2.1027, 'grad_norm': 0.0, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:38<09:24, 3.67s/it] 71%|███████ | 367/520 [22:42<09:30, 3.73s/it] {'loss': 2.1701, 'grad_norm': 0.0, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:42<09:30, 3.73s/it] 71%|███████ | 368/520 [22:46<09:31, 3.76s/it] {'loss': 2.1175, 'grad_norm': 0.0, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:46<09:31, 3.76s/it] 71%|███████ | 369/520 [22:49<09:21, 3.72s/it] {'loss': 1.7789, 'grad_norm': 0.0, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:49<09:21, 3.72s/it] 71%|███████ | 370/520 [22:53<09:14, 3.69s/it] {'loss': 2.0015, 'grad_norm': 0.0, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:53<09:14, 3.69s/it] 71%|███████▏ | 371/520 [22:57<09:14, 3.72s/it] {'loss': 2.1704, 'grad_norm': 0.0, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [22:57<09:14, 3.72s/it] 72%|███████▏ | 372/520 [23:00<09:15, 3.76s/it] {'loss': 1.8294, 'grad_norm': 0.0, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:00<09:15, 3.76s/it] 72%|███████▏ | 373/520 [23:04<09:15, 3.78s/it] {'loss': 2.0181, 'grad_norm': 0.0, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:04<09:15, 3.78s/it] 72%|███████▏ | 374/520 [23:08<09:13, 3.79s/it] {'loss': 2.1018, 'grad_norm': 0.0, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:08<09:13, 3.79s/it] 72%|███████▏ | 375/520 [23:12<09:08, 3.78s/it] {'loss': 2.1132, 'grad_norm': 0.0, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:12<09:08, 3.78s/it] 72%|███████▏ | 376/520 [23:15<08:58, 3.74s/it] {'loss': 2.0573, 'grad_norm': 0.0, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:16<08:58, 3.74s/it] 72%|███████▎ | 377/520 [23:19<08:50, 3.71s/it] {'loss': 2.0899, 'grad_norm': 0.0, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:19<08:50, 3.71s/it] 73%|███████▎ | 378/520 [23:23<08:44, 3.69s/it] {'loss': 2.0289, 'grad_norm': 0.0, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:23<08:44, 3.69s/it] 73%|███████▎ | 379/520 [23:26<08:39, 3.68s/it] {'loss': 1.9774, 'grad_norm': 0.0, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:26<08:39, 3.68s/it] 73%|███████▎ | 380/520 [23:30<08:35, 3.68s/it] {'loss': 1.8319, 'grad_norm': 0.0, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:30<08:35, 3.68s/it] 73%|███████▎ | 381/520 [23:34<08:30, 3.67s/it] {'loss': 2.0371, 'grad_norm': 0.0, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:34<08:30, 3.67s/it] 73%|███████▎ | 382/520 [23:37<08:28, 3.68s/it] {'loss': 1.9153, 'grad_norm': 0.0, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:38<08:28, 3.68s/it] 74%|███████▎ | 383/520 [23:41<08:22, 3.67s/it] {'loss': 2.2443, 'grad_norm': 0.0, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:41<08:22, 3.67s/it] 74%|███████▍ | 384/520 [23:45<08:18, 3.66s/it] {'loss': 1.6572, 'grad_norm': 0.0, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:45<08:18, 3.66s/it] 74%|███████▍ | 385/520 [23:48<08:14, 3.66s/it] {'loss': 1.9484, 'grad_norm': 0.0, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:48<08:14, 3.66s/it] 74%|███████▍ | 386/520 [23:52<08:11, 3.66s/it] {'loss': 2.0001, 'grad_norm': 0.0, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:52<08:11, 3.66s/it] 74%|███████▍ | 387/520 [23:56<08:08, 3.67s/it] {'loss': 1.7967, 'grad_norm': 0.0, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [23:56<08:08, 3.67s/it] 75%|███████▍ | 388/520 [24:00<08:05, 3.68s/it] {'loss': 2.1252, 'grad_norm': 0.0, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:00<08:05, 3.68s/it] 75%|███████▍ | 389/520 [24:03<08:07, 3.72s/it] {'loss': 2.2819, 'grad_norm': 0.0, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:03<08:07, 3.72s/it] 75%|███████▌ | 390/520 [24:07<08:09, 3.77s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:07<08:09, 3.77s/it] 75%|███████▌ | 391/520 [24:11<08:09, 3.79s/it] {'loss': 2.0751, 'grad_norm': 0.0, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:11<08:09, 3.79s/it] 75%|███████▌ | 392/520 [24:15<08:08, 3.81s/it] {'loss': 2.0834, 'grad_norm': 0.0, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:15<08:08, 3.81s/it] 76%|███████▌ | 393/520 [24:19<08:05, 3.82s/it] {'loss': 1.6935, 'grad_norm': 0.0, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:19<08:05, 3.82s/it] 76%|███████▌ | 394/520 [24:23<08:02, 3.83s/it] {'loss': 2.1218, 'grad_norm': 0.0, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:23<08:02, 3.83s/it] 76%|███████▌ | 395/520 [24:26<07:58, 3.83s/it] {'loss': 2.1493, 'grad_norm': 0.0, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:26<07:58, 3.83s/it] 76%|███████▌ | 396/520 [24:30<07:54, 3.82s/it] {'loss': 2.0961, 'grad_norm': 0.0, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:30<07:54, 3.82s/it] 76%|███████▋ | 397/520 [24:34<07:51, 3.84s/it] {'loss': 2.0472, 'grad_norm': 0.0, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:34<07:51, 3.84s/it] 77%|███████▋ | 398/520 [24:38<07:46, 3.83s/it] {'loss': 2.2098, 'grad_norm': 0.0, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:38<07:46, 3.83s/it] 77%|███████▋ | 399/520 [24:42<07:36, 3.77s/it] {'loss': 1.8453, 'grad_norm': 0.0, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:42<07:36, 3.77s/it] 77%|███████▋ | 400/520 [24:45<07:29, 3.75s/it] {'loss': 1.8965, 'grad_norm': 0.0, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:45<07:29, 3.75s/it] 77%|███████▋ | 401/520 [24:49<07:21, 3.71s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:49<07:21, 3.71s/it] 77%|███████▋ | 402/520 [24:53<07:15, 3.69s/it] {'loss': 2.1351, 'grad_norm': 0.0, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:53<07:15, 3.69s/it] 78%|███████▊ | 403/520 [24:56<07:10, 3.68s/it] {'loss': 2.1219, 'grad_norm': 0.0, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [24:56<07:10, 3.68s/it] 78%|███████▊ | 404/520 [25:00<07:05, 3.67s/it] {'loss': 2.2818, 'grad_norm': 0.0, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:00<07:05, 3.67s/it] 78%|███████▊ | 405/520 [25:03<07:01, 3.67s/it] {'loss': 1.875, 'grad_norm': 0.0, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:03<07:01, 3.67s/it] 78%|███████▊ | 406/520 [25:07<06:57, 3.66s/it] {'loss': 2.1806, 'grad_norm': 0.0, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:07<06:57, 3.66s/it] 78%|███████▊ | 407/520 [25:11<06:53, 3.66s/it] {'loss': 2.0986, 'grad_norm': 0.0, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:11<06:53, 3.66s/it] 78%|███████▊ | 408/520 [25:14<06:49, 3.66s/it] {'loss': 2.1517, 'grad_norm': 0.0, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:14<06:49, 3.66s/it] 79%|███████▊ | 409/520 [25:18<06:45, 3.66s/it] {'loss': 2.2385, 'grad_norm': 0.0, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:18<06:45, 3.66s/it] 79%|███████▉ | 410/520 [25:22<06:41, 3.65s/it] {'loss': 2.1727, 'grad_norm': 0.0, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:22<06:41, 3.65s/it] 79%|███████▉ | 411/520 [25:25<06:38, 3.66s/it] {'loss': 2.195, 'grad_norm': 0.0, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:25<06:38, 3.66s/it] 79%|███████▉ | 412/520 [25:29<06:35, 3.66s/it] {'loss': 2.0965, 'grad_norm': 0.0, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:29<06:35, 3.66s/it] 79%|███████▉ | 413/520 [25:33<06:36, 3.70s/it] {'loss': 1.916, 'grad_norm': 0.0, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:33<06:36, 3.70s/it] 80%|███████▉ | 414/520 [25:37<06:36, 3.74s/it] {'loss': 1.757, 'grad_norm': 0.0, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:37<06:36, 3.74s/it] 80%|███████▉ | 415/520 [25:41<06:35, 3.77s/it] {'loss': 2.0894, 'grad_norm': 0.0, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:41<06:35, 3.77s/it] 80%|████████ | 416/520 [25:44<06:33, 3.79s/it] {'loss': 2.3404, 'grad_norm': 0.0, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:44<06:33, 3.79s/it] 80%|████████ | 417/520 [25:48<06:31, 3.80s/it] {'loss': 2.0376, 'grad_norm': 0.0, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:48<06:31, 3.80s/it] 80%|████████ | 418/520 [25:52<06:28, 3.81s/it] {'loss': 1.9876, 'grad_norm': 0.0, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:52<06:28, 3.81s/it] 81%|████████ | 419/520 [25:56<06:25, 3.81s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:56<06:25, 3.81s/it] 81%|████████ | 420/520 [26:00<06:21, 3.82s/it] {'loss': 2.1783, 'grad_norm': 0.0, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:00<06:21, 3.82s/it] 81%|████████ | 421/520 [26:04<06:18, 3.82s/it] {'loss': 2.3788, 'grad_norm': 0.0, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:04<06:18, 3.82s/it] 81%|████████ | 422/520 [26:07<06:15, 3.83s/it] {'loss': 2.1751, 'grad_norm': 0.0, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:07<06:15, 3.83s/it] 81%|████████▏ | 423/520 [26:11<06:07, 3.79s/it] {'loss': 2.3239, 'grad_norm': 0.0, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:11<06:07, 3.79s/it] 82%|████████▏ | 424/520 [26:15<06:00, 3.75s/it] {'loss': 1.8431, 'grad_norm': 0.0, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:15<06:00, 3.75s/it] 82%|████████▏ | 425/520 [26:18<05:54, 3.73s/it] {'loss': 2.0338, 'grad_norm': 0.0, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:18<05:54, 3.73s/it] 82%|████████▏ | 426/520 [26:22<05:48, 3.70s/it] {'loss': 2.2809, 'grad_norm': 0.0, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:22<05:48, 3.70s/it] 82%|████████▏ | 427/520 [26:26<05:42, 3.69s/it] {'loss': 1.9615, 'grad_norm': 0.0, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:26<05:42, 3.69s/it] 82%|████████▏ | 428/520 [26:29<05:38, 3.68s/it] {'loss': 2.179, 'grad_norm': 0.0, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:29<05:38, 3.68s/it] 82%|████████▎ | 429/520 [26:33<05:35, 3.69s/it] {'loss': 2.1882, 'grad_norm': 0.0, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:33<05:35, 3.69s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:37<05:36, 3.74s/it] {'loss': 2.0206, 'grad_norm': 0.0, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:37<05:36, 3.74s/it] 83%|████████▎ | 431/520 [26:41<05:36, 3.78s/it] {'loss': 1.8737, 'grad_norm': 0.0, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:41<05:36, 3.78s/it] 83%|████████▎ | 432/520 [26:45<05:34, 3.80s/it] {'loss': 2.0893, 'grad_norm': 0.0, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:45<05:34, 3.80s/it] 83%|████████▎ | 433/520 [26:48<05:27, 3.76s/it] {'loss': 2.1446, 'grad_norm': 0.0, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:48<05:27, 3.76s/it] 83%|████████▎ | 434/520 [26:52<05:19, 3.72s/it] {'loss': 2.162, 'grad_norm': 0.0, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:52<05:19, 3.72s/it] 84%|████████▎ | 435/520 [26:56<05:13, 3.68s/it] {'loss': 2.1714, 'grad_norm': 0.0, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:56<05:13, 3.68s/it] 84%|████████▍ | 436/520 [26:59<05:08, 3.67s/it] {'loss': 2.1073, 'grad_norm': 0.0, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:59<05:08, 3.67s/it] 84%|████████▍ | 437/520 [27:03<05:03, 3.66s/it] {'loss': 2.1399, 'grad_norm': 0.0, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:03<05:03, 3.66s/it] 84%|████████▍ | 438/520 [27:06<04:59, 3.66s/it] {'loss': 2.1034, 'grad_norm': 0.0, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:06<04:59, 3.66s/it] 84%|████████▍ | 439/520 [27:10<04:55, 3.65s/it] {'loss': 1.7664, 'grad_norm': 0.0, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:10<04:55, 3.65s/it] 85%|████████▍ | 440/520 [27:14<04:51, 3.64s/it] {'loss': 2.0058, 'grad_norm': 0.0, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:14<04:51, 3.64s/it] 85%|████████▍ | 441/520 [27:17<04:47, 3.64s/it] {'loss': 1.8248, 'grad_norm': 0.0, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:17<04:47, 3.64s/it] 85%|████████▌ | 442/520 [27:21<04:43, 3.64s/it] {'loss': 2.3179, 'grad_norm': 0.0, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:21<04:43, 3.64s/it] 85%|████████▌ | 443/520 [27:25<04:39, 3.63s/it] {'loss': 2.0141, 'grad_norm': 0.0, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:25<04:39, 3.63s/it] 85%|████████▌ | 444/520 [27:28<04:36, 3.64s/it] {'loss': 1.9937, 'grad_norm': 0.0, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:28<04:36, 3.64s/it] 86%|████████▌ | 445/520 [27:32<04:31, 3.62s/it] {'loss': 1.9637, 'grad_norm': 0.0, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:32<04:31, 3.62s/it] 86%|████████▌ | 446/520 [27:35<04:27, 3.62s/it] {'loss': 1.8401, 'grad_norm': 0.0, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:35<04:27, 3.62s/it] 86%|████████▌ | 447/520 [27:39<04:24, 3.62s/it] {'loss': 2.1475, 'grad_norm': 0.0, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:39<04:24, 3.62s/it] 86%|████████▌ | 448/520 [27:43<04:20, 3.61s/it] {'loss': 2.0884, 'grad_norm': 0.0, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:43<04:20, 3.61s/it] 86%|████████▋ | 449/520 [27:46<04:16, 3.61s/it] {'loss': 1.9783, 'grad_norm': 0.0, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:46<04:16, 3.61s/it] 87%|████████▋ | 450/520 [27:50<04:12, 3.61s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:50<04:12, 3.61s/it] 87%|████████▋ | 451/520 [27:53<04:09, 3.61s/it] {'loss': 2.1608, 'grad_norm': 0.0, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:53<04:09, 3.61s/it] 87%|████████▋ | 452/520 [27:57<04:05, 3.61s/it] {'loss': 1.8367, 'grad_norm': 0.0, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:57<04:05, 3.61s/it] 87%|████████▋ | 453/520 [28:01<04:01, 3.61s/it] {'loss': 1.9767, 'grad_norm': 0.0, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:01<04:01, 3.61s/it] 87%|████████▋ | 454/520 [28:04<03:58, 3.61s/it] {'loss': 2.0911, 'grad_norm': 0.0, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:04<03:58, 3.61s/it] 88%|████████▊ | 455/520 [28:08<03:57, 3.65s/it] {'loss': 2.0563, 'grad_norm': 0.0, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:08<03:57, 3.65s/it] 88%|████████▊ | 456/520 [28:12<03:56, 3.70s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:12<03:56, 3.70s/it] 88%|████████▊ | 457/520 [28:16<03:55, 3.75s/it] {'loss': 1.7164, 'grad_norm': 0.0, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:16<03:55, 3.75s/it] 88%|████████▊ | 458/520 [28:20<03:54, 3.78s/it] {'loss': 2.2316, 'grad_norm': 0.0, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:20<03:54, 3.78s/it] 88%|████████▊ | 459/520 [28:23<03:52, 3.80s/it] {'loss': 2.0823, 'grad_norm': 0.0, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:23<03:52, 3.80s/it] 88%|████████▊ | 460/520 [28:27<03:48, 3.81s/it] {'loss': 2.1604, 'grad_norm': 0.0, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:27<03:48, 3.81s/it] 89%|████████▊ | 461/520 [28:31<03:45, 3.83s/it] {'loss': 1.499, 'grad_norm': 0.0, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:31<03:45, 3.83s/it] 89%|████████▉ | 462/520 [28:35<03:42, 3.83s/it] {'loss': 1.9033, 'grad_norm': 0.0, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:35<03:42, 3.83s/it] 89%|████████▉ | 463/520 [28:39<03:38, 3.83s/it] {'loss': 2.3309, 'grad_norm': 0.0, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:39<03:38, 3.83s/it] 89%|████████▉ | 464/520 [28:43<03:33, 3.81s/it] {'loss': 2.1128, 'grad_norm': 0.0, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:43<03:33, 3.81s/it] 89%|████████▉ | 465/520 [28:46<03:25, 3.74s/it] {'loss': 2.1251, 'grad_norm': 0.0, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:46<03:25, 3.74s/it] 90%|████████▉ | 466/520 [28:50<03:19, 3.70s/it] {'loss': 1.9657, 'grad_norm': 0.0, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:50<03:19, 3.70s/it] 90%|████████▉ | 467/520 [28:53<03:14, 3.67s/it] {'loss': 1.8888, 'grad_norm': 0.0, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:53<03:14, 3.67s/it] 90%|█████████ | 468/520 [28:57<03:10, 3.65s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [28:57<03:10, 3.65s/it] 90%|█████████ | 469/520 [29:01<03:05, 3.64s/it] {'loss': 2.1097, 'grad_norm': 0.0, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:01<03:05, 3.64s/it] 90%|█████████ | 470/520 [29:04<03:01, 3.62s/it] {'loss': 2.0168, 'grad_norm': 0.0, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:04<03:01, 3.62s/it] 91%|█████████ | 471/520 [29:08<02:57, 3.62s/it] {'loss': 2.2295, 'grad_norm': 0.0, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:08<02:57, 3.62s/it] 91%|█████████ | 472/520 [29:11<02:53, 3.62s/it] {'loss': 2.1844, 'grad_norm': 0.0, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:11<02:53, 3.62s/it] 91%|█████████ | 473/520 [29:15<02:49, 3.61s/it] {'loss': 2.2132, 'grad_norm': 0.0, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:15<02:49, 3.61s/it] 91%|█████████ | 474/520 [29:19<02:45, 3.61s/it] {'loss': 1.9064, 'grad_norm': 0.0, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:19<02:45, 3.61s/it] 91%|█████████▏| 475/520 [29:22<02:42, 3.61s/it] {'loss': 1.8533, 'grad_norm': 0.0, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:22<02:42, 3.61s/it] 92%|█████████▏| 476/520 [29:26<02:38, 3.61s/it] {'loss': 2.165, 'grad_norm': 0.0, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:26<02:38, 3.61s/it] 92%|█████████▏| 477/520 [29:29<02:35, 3.61s/it] {'loss': 2.156, 'grad_norm': 0.0, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:29<02:35, 3.61s/it] 92%|█████████▏| 478/520 [29:33<02:31, 3.61s/it] {'loss': 2.069, 'grad_norm': 0.0, 'learning_rate': 0.00340741737109318, 'epoch': 0.92} + 92%|█████████▏| 478/520 [29:33<02:31, 3.61s/it] 92%|█████████▏| 479/520 [29:37<02:28, 3.61s/it] {'loss': 1.9471, 'grad_norm': 0.0, 'learning_rate': 0.003247964765680389, 'epoch': 0.92} + 92%|█████████▏| 479/520 [29:37<02:28, 3.61s/it] 92%|█████████▏| 480/520 [29:40<02:24, 3.62s/it] {'loss': 1.9655, 'grad_norm': 0.0, 'learning_rate': 0.0030922713770922153, 'epoch': 0.92} + 92%|█████████▏| 480/520 [29:40<02:24, 3.62s/it] 92%|█████████▎| 481/520 [29:44<02:21, 3.62s/it] {'loss': 1.8537, 'grad_norm': 0.0, 'learning_rate': 0.0029403432546609046, 'epoch': 0.93} + 92%|█████████▎| 481/520 [29:44<02:21, 3.62s/it] 93%|█████████▎| 482/520 [29:47<02:17, 3.61s/it] {'loss': 1.8943, 'grad_norm': 0.0, 'learning_rate': 0.0027921863014225504, 'epoch': 0.93} + 93%|█████████▎| 482/520 [29:47<02:17, 3.61s/it] 93%|█████████▎| 483/520 [29:51<02:14, 3.62s/it] {'loss': 2.0377, 'grad_norm': 0.0, 'learning_rate': 0.002647806273887665, 'epoch': 0.93} + 93%|█████████▎| 483/520 [29:51<02:14, 3.62s/it] 93%|█████████▎| 484/520 [29:55<02:10, 3.63s/it] {'loss': 2.1901, 'grad_norm': 0.0, 'learning_rate': 0.0025072087818176383, 'epoch': 0.93} + 93%|█████████▎| 484/520 [29:55<02:10, 3.63s/it] 93%|█████████▎| 485/520 [29:58<02:06, 3.63s/it] {'loss': 2.0144, 'grad_norm': 0.0, 'learning_rate': 0.002370399288006664, 'epoch': 0.93} + 93%|█████████▎| 485/520 [29:58<02:06, 3.63s/it] 93%|█████████▎| 486/520 [30:02<02:03, 3.62s/it] {'loss': 2.0347, 'grad_norm': 0.0, 'learning_rate': 0.0022373831080695463, 'epoch': 0.93} + 93%|█████████▎| 486/520 [30:02<02:03, 3.62s/it] 94%|█████████▎| 487/520 [30:06<01:59, 3.63s/it] {'loss': 2.0192, 'grad_norm': 0.0, 'learning_rate': 0.0021081654102351635, 'epoch': 0.94} + 94%|█████████▎| 487/520 [30:06<01:59, 3.63s/it] 94%|█████████▍| 488/520 [30:09<01:55, 3.62s/it] {'loss': 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0.0014128981481764114, 'epoch': 0.95} + 95%|█████████▍| 493/520 [30:27<01:38, 3.63s/it] 95%|█████████▌| 494/520 [30:31<01:34, 3.63s/it] {'loss': 2.001, 'grad_norm': 0.0, 'learning_rate': 0.0013104021143278911, 'epoch': 0.95} + 95%|█████████▌| 494/520 [30:31<01:34, 3.63s/it] 95%|█████████▌| 495/520 [30:35<01:30, 3.62s/it] {'loss': 2.0557, 'grad_norm': 0.0, 'learning_rate': 0.0012117405796285285, 'epoch': 0.95} + 95%|█████████▌| 495/520 [30:35<01:30, 3.62s/it] 95%|█████████▌| 496/520 [30:38<01:27, 3.63s/it] {'loss': 2.1532, 'grad_norm': 0.0, 'learning_rate': 0.0011169173774871477, 'epoch': 0.95} + 95%|█████████▌| 496/520 [30:38<01:27, 3.63s/it] 96%|█████████▌| 497/520 [30:42<01:23, 3.62s/it] {'loss': 1.8146, 'grad_norm': 0.0, 'learning_rate': 0.0010259361921774012, 'epoch': 0.96} + 96%|█████████▌| 497/520 [30:42<01:23, 3.62s/it] 96%|█████████▌| 498/520 [30:45<01:19, 3.62s/it] {'loss': 2.109, 'grad_norm': 0.0, 'learning_rate': 0.000938800558694719, 'epoch': 0.96} + 96%|█████████▌| 498/520 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'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:25<00:39, 3.61s/it] 98%|█████████▊| 510/520 [31:29<00:36, 3.62s/it] {'loss': 2.1279, 'grad_norm': 0.0, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:29<00:36, 3.62s/it] 98%|█████████▊| 511/520 [31:32<00:32, 3.61s/it] {'loss': 2.0474, 'grad_norm': 0.0, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:32<00:32, 3.61s/it] 98%|█████████▊| 512/520 [31:36<00:28, 3.61s/it] {'loss': 2.0024, 'grad_norm': 0.0, 'learning_rate': 0.00012430787810776555, 'epoch': 0.98} + 98%|█████████▊| 512/520 [31:36<00:28, 3.61s/it] 99%|█████████▊| 513/520 [31:40<00:25, 3.62s/it] {'loss': 2.1995, 'grad_norm': 0.0, 'learning_rate': 9.517784181422018e-05, 'epoch': 0.99} + 99%|█████████▊| 513/520 [31:40<00:25, 3.62s/it] 99%|█████████▉| 514/520 [31:43<00:21, 3.61s/it] {'loss': 2.0174, 'grad_norm': 0.0, 'learning_rate': 6.992952116013917e-05, 'epoch': 0.99} + 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100%|██████████| 520/520 [32:06<00:00, 3.84s/it] {'loss': 1.7443, 'grad_norm': 0.0, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:06<00:00, 3.84s/it] {'train_runtime': 1926.1281, 'train_samples_per_second': 34.54, 'train_steps_per_second': 0.27, 'train_loss': 2.066992656771953, 'epoch': 1.0} + 100%|██████████| 520/520 [32:06<00:00, 3.84s/it] 100%|██████████| 520/520 [32:06<00:00, 3.70s/it] +[2025-10-18 01:22:39,018] [INFO] [launch.py:348:main] Process 810726 exits successfully. +[2025-10-18 01:22:39,018] [INFO] [launch.py:348:main] Process 810723 exits successfully. +[2025-10-18 01:22:39,019] [INFO] [launch.py:348:main] Process 810727 exits successfully. +[2025-10-18 01:22:39,019] [INFO] [launch.py:348:main] Process 810724 exits successfully. +[2025-10-18 01:22:40,021] [INFO] [launch.py:348:main] Process 810725 exits successfully. +[2025-10-18 01:22:40,021] [INFO] [launch.py:348:main] Process 810721 exits successfully. +[2025-10-18 01:22:40,022] [INFO] [launch.py:348:main] Process 810722 exits successfully. +[2025-10-18 01:22:43,025] [INFO] [launch.py:348:main] Process 810720 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.1_2e-1_connector-9.0_1.1_2e-1_ablation_20251018_004858.log +Timestamp: 2025-10-18 01:22:45 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation_20251018_012245.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation_20251018_012245.log new file mode 100644 index 0000000000000000000000000000000000000000..31646b2572bbdb941668c0d8f37289a4ffe41aab --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation_20251018_012245.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation_20251018_012245.log +Timestamp: 2025-10-18 01:22:45 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:22:48,267] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:22:52,017] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 01:22:52,018] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 1.3 --temperature_mlp_text 1.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 1.3 --temperature_mlp_vision 1.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 1.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:22:54,578] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:22:55,638] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 01:22:55,638] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 01:22:55,638] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 01:22:55,638] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 01:22:55,638] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 01:22:55,638] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 01:22:55,638] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 01:22:55,640] [INFO] [launch.py:253:main] process 832674 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,642] [INFO] [launch.py:253:main] process 832675 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,644] [INFO] [launch.py:253:main] process 832676 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,646] [INFO] [launch.py:253:main] process 832677 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,648] [INFO] [launch.py:253:main] process 832678 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,649] [INFO] [launch.py:253:main] process 832679 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,651] [INFO] [launch.py:253:main] process 832680 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:22:55,653] [INFO] [launch.py:253:main] process 832681 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.3', '--temperature_mlp_text', '1.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.3', '--temperature_mlp_vision', '1.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:23:02,143] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,472] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,472] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,474] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,555] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,556] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,560] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,579] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,590] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:23:02,897] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,897] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,899] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,975] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,975] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 01:23:02,977] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:02,997] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:23:03,007] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.3, 'temperature_mlp': 1.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.3, + "temperature_mlp": 1.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:832674:832674 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:832681:832681 [7] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:832680:832680 [6] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:832675:832675 [1] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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524288 +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832677:834270 [3] NCCL INFO ncclCommInitRank comm 0x5571af6cf5a0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832676:834273 [2] NCCL INFO ncclCommInitRank comm 0x560872788ba0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832674:834266 [0] NCCL INFO ncclCommInitRank comm 0x5575efc902d0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832681:834267 [7] NCCL INFO ncclCommInitRank comm 0x5619215419c0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832678:834272 [4] NCCL INFO ncclCommInitRank comm 0x564a5a118ee0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832680:834268 [6] NCCL INFO ncclCommInitRank comm 0x5587d7f6ee30 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832679:834271 [5] NCCL INFO ncclCommInitRank comm 0x55c33fc56a40 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x3d8ff26ab463c970 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:832675:834269 [1] NCCL INFO ncclCommInitRank comm 0x55a1fa633670 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x3d8ff26ab463c970 - Init COMPLETE +[2025-10-18 01:23:46,689] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 01:23:48,469] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 01:24:07,051 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 01:24:07,058 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters 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+language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:007->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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per peer +ywang29-vrdb-test1-worker-0:832678:839250 [4] NCCL INFO ncclCommInitRank comm 0x7f1f7806b200 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832674:839249 [0] NCCL INFO ncclCommInitRank comm 0x7f521406b7a0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832681:839251 [7] NCCL INFO ncclCommInitRank comm 0x7f07ac06a430 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832679:839253 [5] NCCL INFO ncclCommInitRank comm 0x7f70b006b0c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832677:839255 [3] NCCL INFO ncclCommInitRank comm 0x7f0a7c06b080 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832675:839254 [1] NCCL INFO ncclCommInitRank comm 0x7f2b4006b800 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832676:839256 [2] NCCL INFO ncclCommInitRank comm 0x7f4fd406ab50 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x5f544daa05fe9756 - Init COMPLETE +ywang29-vrdb-test1-worker-0:832680:839252 [6] NCCL INFO ncclCommInitRank comm 0x7f4a1c06b980 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x5f544daa05fe9756 - Init COMPLETE + 0%| | 1/520 [00:13<2:01:04, 14.00s/it] {'loss': 2.0497, 'grad_norm': 0.0, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:01:04, 14.00s/it] 0%| | 2/520 [00:17<1:08:26, 7.93s/it] {'loss': 2.06, 'grad_norm': 0.0, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:08:26, 7.93s/it] 1%| | 3/520 [00:21<51:41, 6.00s/it] {'loss': 2.1958, 'grad_norm': 0.0, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<51:41, 6.00s/it] 1%| | 4/520 [00:25<44:02, 5.12s/it] 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If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:00<34:29, 4.08s/it] {'loss': 2.0728, 'grad_norm': 0.0, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:00<34:29, 4.08s/it] 3%|▎ | 14/520 [01:04<33:54, 4.02s/it] {'loss': 2.1118, 'grad_norm': 0.0, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:04<33:54, 4.02s/it] 3%|▎ | 15/520 [01:08<33:28, 3.98s/it] {'loss': 1.7478, 'grad_norm': 0.0, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:08<33:28, 3.98s/it] 3%|▎ | 16/520 [01:12<33:06, 3.94s/it] {'loss': 1.8954, 'grad_norm': 0.0, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:12<33:06, 3.94s/it] 3%|▎ | 17/520 [01:16<32:51, 3.92s/it] {'loss': 2.1158, 'grad_norm': 0.0, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:16<32:51, 3.92s/it] 3%|▎ | 18/520 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[20:39<11:52, 3.69s/it] {'loss': 2.061, 'grad_norm': 0.0, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:39<11:52, 3.69s/it] 63%|██████▎ | 328/520 [20:42<11:47, 3.68s/it] {'loss': 2.1111, 'grad_norm': 0.0, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:42<11:47, 3.68s/it] 63%|██████▎ | 329/520 [20:46<11:45, 3.69s/it] {'loss': 1.9436, 'grad_norm': 0.0, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:46<11:45, 3.69s/it] 63%|██████▎ | 330/520 [20:50<11:40, 3.69s/it] {'loss': 2.1281, 'grad_norm': 0.0, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:50<11:40, 3.69s/it] 64%|██████▎ | 331/520 [20:53<11:35, 3.68s/it] {'loss': 2.1551, 'grad_norm': 0.0, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:53<11:35, 3.68s/it] 64%|██████▍ | 332/520 [20:57<11:31, 3.68s/it] {'loss': 1.8491, 'grad_norm': 0.0, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:57<11:31, 3.68s/it] 64%|██████▍ | 333/520 [21:01<11:27, 3.68s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [21:01<11:27, 3.68s/it] 64%|██████▍ | 334/520 [21:05<11:25, 3.69s/it] {'loss': 2.1157, 'grad_norm': 0.0, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [21:05<11:25, 3.69s/it] 64%|██████▍ | 335/520 [21:08<11:23, 3.70s/it] {'loss': 2.013, 'grad_norm': 0.0, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [21:08<11:23, 3.70s/it] 65%|██████▍ | 336/520 [21:12<11:19, 3.69s/it] {'loss': 2.1874, 'grad_norm': 0.0, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:12<11:19, 3.69s/it] 65%|██████▍ | 337/520 [21:16<11:15, 3.69s/it] {'loss': 2.2477, 'grad_norm': 0.0, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:16<11:15, 3.69s/it] 65%|██████▌ | 338/520 [21:19<11:10, 3.69s/it] {'loss': 2.1774, 'grad_norm': 0.0, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:19<11:10, 3.69s/it] 65%|██████▌ | 339/520 [21:23<11:06, 3.68s/it] {'loss': 2.126, 'grad_norm': 0.0, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:23<11:06, 3.68s/it] 65%|██████▌ | 340/520 [21:27<11:02, 3.68s/it] {'loss': 2.0845, 'grad_norm': 0.0, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:27<11:02, 3.68s/it] 66%|██████▌ | 341/520 [21:30<10:59, 3.69s/it] {'loss': 2.094, 'grad_norm': 0.0, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:30<10:59, 3.69s/it] 66%|██████▌ | 342/520 [21:34<10:55, 3.68s/it] {'loss': 2.0199, 'grad_norm': 0.0, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:34<10:55, 3.68s/it] 66%|██████▌ | 343/520 [21:38<10:52, 3.68s/it] {'loss': 1.7182, 'grad_norm': 0.0, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:38<10:52, 3.68s/it] 66%|██████▌ | 344/520 [21:41<10:49, 3.69s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:41<10:49, 3.69s/it] 66%|██████▋ | 345/520 [21:45<10:43, 3.68s/it] {'loss': 2.2588, 'grad_norm': 0.0, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:45<10:43, 3.68s/it] 67%|██████▋ | 346/520 [21:49<10:38, 3.67s/it] {'loss': 1.859, 'grad_norm': 0.0, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:49<10:38, 3.67s/it] 67%|██████▋ | 347/520 [21:52<10:36, 3.68s/it] {'loss': 1.9277, 'grad_norm': 0.0, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:52<10:36, 3.68s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:56<10:31, 3.67s/it] {'loss': 2.405, 'grad_norm': 0.0, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:56<10:31, 3.67s/it] 67%|██████▋ | 349/520 [22:00<10:26, 3.66s/it] {'loss': 2.2236, 'grad_norm': 0.0, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [22:00<10:26, 3.66s/it] 67%|██████▋ | 350/520 [22:03<10:21, 3.66s/it] {'loss': 2.1184, 'grad_norm': 0.0, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [22:03<10:21, 3.66s/it] 68%|██████▊ | 351/520 [22:07<10:19, 3.67s/it] {'loss': 2.0414, 'grad_norm': 0.0, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:07<10:19, 3.67s/it] 68%|██████▊ | 352/520 [22:11<10:16, 3.67s/it] {'loss': 2.0824, 'grad_norm': 0.0, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:11<10:16, 3.67s/it] 68%|██████▊ | 353/520 [22:14<10:12, 3.67s/it] {'loss': 1.8115, 'grad_norm': 0.0, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:14<10:12, 3.67s/it] 68%|██████▊ | 354/520 [22:18<10:07, 3.66s/it] {'loss': 1.8797, 'grad_norm': 0.0, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:18<10:07, 3.66s/it] 68%|██████▊ | 355/520 [22:22<10:02, 3.65s/it] {'loss': 2.0561, 'grad_norm': 0.0, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:22<10:02, 3.65s/it] 68%|██████▊ | 356/520 [22:25<09:58, 3.65s/it] {'loss': 2.2531, 'grad_norm': 0.0, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:25<09:58, 3.65s/it] 69%|██████▊ | 357/520 [22:29<09:53, 3.64s/it] {'loss': 2.0294, 'grad_norm': 0.0, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:29<09:53, 3.64s/it] 69%|██████▉ | 358/520 [22:33<09:51, 3.65s/it] {'loss': 2.0531, 'grad_norm': 0.0, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:33<09:51, 3.65s/it] 69%|██████▉ | 359/520 [22:36<09:49, 3.66s/it] {'loss': 2.011, 'grad_norm': 0.0, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:36<09:49, 3.66s/it] 69%|██████▉ | 360/520 [22:40<09:46, 3.66s/it] {'loss': 1.9948, 'grad_norm': 0.0, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:40<09:46, 3.66s/it] 69%|██████▉ | 361/520 [22:44<09:49, 3.71s/it] {'loss': 1.7504, 'grad_norm': 0.0, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:44<09:49, 3.71s/it] 70%|██████▉ | 362/520 [22:48<09:53, 3.76s/it] {'loss': 2.2058, 'grad_norm': 0.0, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:48<09:53, 3.76s/it] 70%|██████▉ | 363/520 [22:52<09:57, 3.81s/it] {'loss': 2.0753, 'grad_norm': 0.0, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:52<09:57, 3.81s/it] 70%|███████ | 364/520 [22:55<09:59, 3.84s/it] {'loss': 1.9811, 'grad_norm': 0.0, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:55<09:59, 3.84s/it] 70%|███████ | 365/520 [22:59<09:57, 3.85s/it] {'loss': 2.1137, 'grad_norm': 0.0, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:59<09:57, 3.85s/it] 70%|███████ | 366/520 [23:03<09:56, 3.87s/it] {'loss': 2.1027, 'grad_norm': 0.0, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [23:03<09:56, 3.87s/it] 71%|███████ | 367/520 [23:07<09:50, 3.86s/it] {'loss': 2.1701, 'grad_norm': 0.0, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [23:07<09:50, 3.86s/it] 71%|███████ | 368/520 [23:11<09:37, 3.80s/it] {'loss': 2.1175, 'grad_norm': 0.0, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:11<09:37, 3.80s/it] 71%|███████ | 369/520 [23:14<09:27, 3.76s/it] {'loss': 1.7789, 'grad_norm': 0.0, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:14<09:27, 3.76s/it] 71%|███████ | 370/520 [23:18<09:19, 3.73s/it] {'loss': 2.0015, 'grad_norm': 0.0, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:18<09:19, 3.73s/it] 71%|███████▏ | 371/520 [23:22<09:14, 3.72s/it] {'loss': 2.1704, 'grad_norm': 0.0, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:22<09:14, 3.72s/it] 72%|███████▏ | 372/520 [23:25<09:07, 3.70s/it] {'loss': 1.8294, 'grad_norm': 0.0, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:25<09:07, 3.70s/it] 72%|███████▏ | 373/520 [23:29<09:07, 3.73s/it] {'loss': 2.0181, 'grad_norm': 0.0, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:29<09:07, 3.73s/it] 72%|███████▏ | 374/520 [23:33<09:08, 3.75s/it] {'loss': 2.1018, 'grad_norm': 0.0, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:33<09:08, 3.75s/it] 72%|███████▏ | 375/520 [23:37<09:01, 3.73s/it] {'loss': 2.1132, 'grad_norm': 0.0, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:37<09:01, 3.73s/it] 72%|███████▏ | 376/520 [23:40<08:53, 3.71s/it] {'loss': 2.0573, 'grad_norm': 0.0, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:40<08:53, 3.71s/it] 72%|███████▎ | 377/520 [23:44<08:47, 3.69s/it] {'loss': 2.0899, 'grad_norm': 0.0, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:44<08:47, 3.69s/it] 73%|███████▎ | 378/520 [23:48<08:43, 3.69s/it] {'loss': 2.0289, 'grad_norm': 0.0, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:48<08:43, 3.69s/it] 73%|███████▎ | 379/520 [23:51<08:38, 3.68s/it] {'loss': 1.9774, 'grad_norm': 0.0, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:51<08:38, 3.68s/it] 73%|███████▎ | 380/520 [23:55<08:36, 3.69s/it] {'loss': 1.8319, 'grad_norm': 0.0, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:55<08:36, 3.69s/it] 73%|███████▎ | 381/520 [23:59<08:31, 3.68s/it] {'loss': 2.0371, 'grad_norm': 0.0, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:59<08:31, 3.68s/it] 73%|███████▎ | 382/520 [24:02<08:29, 3.69s/it] {'loss': 1.9153, 'grad_norm': 0.0, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [24:02<08:29, 3.69s/it] 74%|███████▎ | 383/520 [24:06<08:23, 3.68s/it] {'loss': 2.2443, 'grad_norm': 0.0, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [24:06<08:23, 3.68s/it] 74%|███████▍ | 384/520 [24:10<08:21, 3.68s/it] {'loss': 1.6572, 'grad_norm': 0.0, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:10<08:21, 3.68s/it] 74%|███████▍ | 385/520 [24:13<08:16, 3.68s/it] {'loss': 1.9484, 'grad_norm': 0.0, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:13<08:16, 3.68s/it] 74%|███████▍ | 386/520 [24:17<08:12, 3.68s/it] {'loss': 2.0001, 'grad_norm': 0.0, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:17<08:12, 3.68s/it] 74%|███████▍ | 387/520 [24:21<08:13, 3.71s/it] {'loss': 1.7967, 'grad_norm': 0.0, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:21<08:13, 3.71s/it] 75%|███████▍ | 388/520 [24:25<08:17, 3.77s/it] {'loss': 2.1252, 'grad_norm': 0.0, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:25<08:17, 3.77s/it] 75%|███████▍ | 389/520 [24:29<08:11, 3.75s/it] {'loss': 2.2819, 'grad_norm': 0.0, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:29<08:11, 3.75s/it] 75%|███████▌ | 390/520 [24:32<08:04, 3.73s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:32<08:04, 3.73s/it] 75%|███████▌ | 391/520 [24:36<07:58, 3.71s/it] {'loss': 2.0751, 'grad_norm': 0.0, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:36<07:58, 3.71s/it] 75%|███████▌ | 392/520 [24:40<07:53, 3.70s/it] {'loss': 2.0834, 'grad_norm': 0.0, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:40<07:53, 3.70s/it] 76%|███████▌ | 393/520 [24:43<07:48, 3.69s/it] {'loss': 1.6935, 'grad_norm': 0.0, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:43<07:48, 3.69s/it] 76%|███████▌ | 394/520 [24:47<07:44, 3.68s/it] {'loss': 2.1218, 'grad_norm': 0.0, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:47<07:44, 3.68s/it] 76%|███████▌ | 395/520 [24:51<07:44, 3.72s/it] {'loss': 2.1493, 'grad_norm': 0.0, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:51<07:44, 3.72s/it] 76%|███████▌ | 396/520 [24:55<07:47, 3.77s/it] {'loss': 2.0961, 'grad_norm': 0.0, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:55<07:47, 3.77s/it] 76%|███████▋ | 397/520 [24:59<07:49, 3.82s/it] {'loss': 2.0472, 'grad_norm': 0.0, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:59<07:49, 3.82s/it] 77%|███████▋ | 398/520 [25:02<07:48, 3.84s/it] {'loss': 2.2098, 'grad_norm': 0.0, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [25:02<07:48, 3.84s/it] 77%|███████▋ | 399/520 [25:06<07:47, 3.87s/it] {'loss': 1.8453, 'grad_norm': 0.0, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [25:06<07:47, 3.87s/it] 77%|███████▋ | 400/520 [25:10<07:45, 3.88s/it] {'loss': 1.8965, 'grad_norm': 0.0, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:10<07:45, 3.88s/it] 77%|███████▋ | 401/520 [25:14<07:42, 3.89s/it] {'loss': 2.0165, 'grad_norm': 0.0, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:14<07:42, 3.89s/it] 77%|███████▋ | 402/520 [25:18<07:34, 3.86s/it] {'loss': 2.1351, 'grad_norm': 0.0, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:18<07:34, 3.86s/it] 78%|███████▊ | 403/520 [25:22<07:28, 3.84s/it] {'loss': 2.1219, 'grad_norm': 0.0, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:22<07:28, 3.84s/it] 78%|███████▊ | 404/520 [25:26<07:23, 3.83s/it] {'loss': 2.2818, 'grad_norm': 0.0, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:26<07:23, 3.83s/it] 78%|███████▊ | 405/520 [25:29<07:14, 3.78s/it] {'loss': 1.875, 'grad_norm': 0.0, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:29<07:14, 3.78s/it] 78%|███████▊ | 406/520 [25:33<07:08, 3.75s/it] {'loss': 2.1806, 'grad_norm': 0.0, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:33<07:08, 3.75s/it] 78%|███████▊ | 407/520 [25:37<07:01, 3.73s/it] {'loss': 2.0986, 'grad_norm': 0.0, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:37<07:01, 3.73s/it] 78%|███████▊ | 408/520 [25:40<06:54, 3.70s/it] {'loss': 2.1517, 'grad_norm': 0.0, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:40<06:54, 3.70s/it] 79%|███████▊ | 409/520 [25:44<06:53, 3.72s/it] {'loss': 2.2385, 'grad_norm': 0.0, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:44<06:53, 3.72s/it] 79%|███████▉ | 410/520 [25:48<06:54, 3.77s/it] {'loss': 2.1727, 'grad_norm': 0.0, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:48<06:54, 3.77s/it] 79%|███████▉ | 411/520 [25:52<06:53, 3.79s/it] {'loss': 2.195, 'grad_norm': 0.0, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:52<06:53, 3.79s/it] 79%|███████▉ | 412/520 [25:56<06:51, 3.81s/it] {'loss': 2.0965, 'grad_norm': 0.0, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:56<06:51, 3.81s/it] 79%|███████▉ | 413/520 [25:59<06:48, 3.82s/it] {'loss': 1.916, 'grad_norm': 0.0, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:59<06:48, 3.82s/it] 80%|███████▉ | 414/520 [26:03<06:47, 3.84s/it] {'loss': 1.757, 'grad_norm': 0.0, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [26:03<06:47, 3.84s/it] 80%|███████▉ | 415/520 [26:07<06:44, 3.85s/it] {'loss': 2.0894, 'grad_norm': 0.0, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [26:07<06:44, 3.85s/it] 80%|████████ | 416/520 [26:11<06:42, 3.87s/it] {'loss': 2.3404, 'grad_norm': 0.0, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [26:11<06:42, 3.87s/it] 80%|████████ | 417/520 [26:15<06:37, 3.86s/it] {'loss': 2.0376, 'grad_norm': 0.0, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:15<06:37, 3.86s/it] 80%|████████ | 418/520 [26:19<06:30, 3.83s/it] {'loss': 1.9876, 'grad_norm': 0.0, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:19<06:30, 3.83s/it] 81%|████████ | 419/520 [26:22<06:22, 3.78s/it] {'loss': 2.2701, 'grad_norm': 0.0, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:22<06:22, 3.78s/it] 81%|████████ | 420/520 [26:26<06:14, 3.75s/it] {'loss': 2.1783, 'grad_norm': 0.0, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:26<06:14, 3.75s/it] 81%|████████ | 421/520 [26:30<06:08, 3.72s/it] {'loss': 2.3788, 'grad_norm': 0.0, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:30<06:08, 3.72s/it] 81%|████████ | 422/520 [26:33<06:03, 3.71s/it] {'loss': 2.1751, 'grad_norm': 0.0, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:33<06:03, 3.71s/it] 81%|████████▏ | 423/520 [26:37<05:59, 3.70s/it] {'loss': 2.3239, 'grad_norm': 0.0, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:37<05:59, 3.70s/it] 82%|████████▏ | 424/520 [26:41<05:55, 3.71s/it] {'loss': 1.8431, 'grad_norm': 0.0, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:41<05:55, 3.71s/it] 82%|████████▏ | 425/520 [26:44<05:50, 3.69s/it] {'loss': 2.0338, 'grad_norm': 0.0, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:44<05:50, 3.69s/it] 82%|████████▏ | 426/520 [26:48<05:45, 3.68s/it] {'loss': 2.2809, 'grad_norm': 0.0, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:48<05:45, 3.68s/it] 82%|████████▏ | 427/520 [26:52<05:44, 3.70s/it] {'loss': 1.9615, 'grad_norm': 0.0, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:52<05:44, 3.70s/it] 82%|████████▏ | 428/520 [26:56<05:42, 3.73s/it] {'loss': 2.179, 'grad_norm': 0.0, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:56<05:42, 3.73s/it] 82%|████████▎ | 429/520 [26:59<05:42, 3.76s/it] {'loss': 2.1882, 'grad_norm': 0.0, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:59<05:42, 3.76s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [27:03<05:39, 3.77s/it] {'loss': 2.0206, 'grad_norm': 0.0, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [27:03<05:39, 3.77s/it] 83%|████████▎ | 431/520 [27:07<05:35, 3.77s/it] {'loss': 1.8737, 'grad_norm': 0.0, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [27:07<05:35, 3.77s/it] 83%|████████▎ | 432/520 [27:11<05:32, 3.78s/it] {'loss': 2.0893, 'grad_norm': 0.0, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [27:11<05:32, 3.78s/it] 83%|████████▎ | 433/520 [27:15<05:28, 3.78s/it] {'loss': 2.1446, 'grad_norm': 0.0, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:15<05:28, 3.78s/it] 83%|████████▎ | 434/520 [27:18<05:25, 3.79s/it] {'loss': 2.162, 'grad_norm': 0.0, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:18<05:25, 3.79s/it] 84%|████████▎ | 435/520 [27:22<05:22, 3.79s/it] {'loss': 2.1714, 'grad_norm': 0.0, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:22<05:22, 3.79s/it] 84%|████████▍ | 436/520 [27:26<05:17, 3.79s/it] {'loss': 2.1073, 'grad_norm': 0.0, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:26<05:17, 3.79s/it] 84%|████████▍ | 437/520 [27:30<05:13, 3.77s/it] {'loss': 2.1399, 'grad_norm': 0.0, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:30<05:13, 3.77s/it] 84%|████████▍ | 438/520 [27:34<05:10, 3.78s/it] {'loss': 2.1034, 'grad_norm': 0.0, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:34<05:10, 3.78s/it] 84%|████████▍ | 439/520 [27:37<05:06, 3.78s/it] {'loss': 1.7664, 'grad_norm': 0.0, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:37<05:06, 3.78s/it] 85%|████████▍ | 440/520 [27:41<05:02, 3.78s/it] {'loss': 2.0058, 'grad_norm': 0.0, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:41<05:02, 3.78s/it] 85%|████████▍ | 441/520 [27:45<04:58, 3.78s/it] {'loss': 1.8248, 'grad_norm': 0.0, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:45<04:58, 3.78s/it] 85%|████████▌ | 442/520 [27:49<04:53, 3.76s/it] {'loss': 2.3179, 'grad_norm': 0.0, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:49<04:53, 3.76s/it] 85%|████████▌ | 443/520 [27:52<04:47, 3.73s/it] {'loss': 2.0141, 'grad_norm': 0.0, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:52<04:47, 3.73s/it] 85%|████████▌ | 444/520 [27:56<04:42, 3.72s/it] {'loss': 1.9937, 'grad_norm': 0.0, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:56<04:42, 3.72s/it] 86%|████████▌ | 445/520 [28:00<04:37, 3.70s/it] {'loss': 1.9637, 'grad_norm': 0.0, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [28:00<04:37, 3.70s/it] 86%|████████▌ | 446/520 [28:03<04:33, 3.70s/it] {'loss': 1.8401, 'grad_norm': 0.0, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [28:03<04:33, 3.70s/it] 86%|████████▌ | 447/520 [28:07<04:29, 3.69s/it] {'loss': 2.1475, 'grad_norm': 0.0, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [28:07<04:29, 3.69s/it] 86%|████████▌ | 448/520 [28:11<04:25, 3.68s/it] {'loss': 2.0884, 'grad_norm': 0.0, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [28:11<04:25, 3.68s/it] 86%|████████▋ | 449/520 [28:14<04:21, 3.69s/it] {'loss': 1.9783, 'grad_norm': 0.0, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:14<04:21, 3.69s/it] 87%|████████▋ | 450/520 [28:18<04:18, 3.69s/it] {'loss': 2.1244, 'grad_norm': 0.0, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:18<04:18, 3.69s/it] 87%|████████▋ | 451/520 [28:22<04:15, 3.71s/it] {'loss': 2.1608, 'grad_norm': 0.0, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:22<04:15, 3.71s/it] 87%|████████▋ | 452/520 [28:26<04:13, 3.73s/it] {'loss': 1.8367, 'grad_norm': 0.0, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:26<04:13, 3.73s/it] 87%|████████▋ | 453/520 [28:29<04:10, 3.74s/it] {'loss': 1.9767, 'grad_norm': 0.0, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:29<04:10, 3.74s/it] 87%|████████▋ | 454/520 [28:33<04:08, 3.77s/it] {'loss': 2.0911, 'grad_norm': 0.0, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:33<04:08, 3.77s/it] 88%|████████▊ | 455/520 [28:37<04:02, 3.73s/it] {'loss': 2.0563, 'grad_norm': 0.0, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:37<04:02, 3.73s/it] 88%|████████▊ | 456/520 [28:40<03:57, 3.71s/it] {'loss': 2.0794, 'grad_norm': 0.0, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:40<03:57, 3.71s/it] 88%|████████▊ | 457/520 [28:44<03:52, 3.70s/it] {'loss': 1.7164, 'grad_norm': 0.0, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:44<03:52, 3.70s/it] 88%|████████▊ | 458/520 [28:48<03:49, 3.70s/it] {'loss': 2.2316, 'grad_norm': 0.0, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:48<03:49, 3.70s/it] 88%|████████▊ | 459/520 [28:51<03:45, 3.69s/it] {'loss': 2.0823, 'grad_norm': 0.0, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:51<03:45, 3.69s/it] 88%|████████▊ | 460/520 [28:55<03:41, 3.69s/it] {'loss': 2.1604, 'grad_norm': 0.0, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:55<03:41, 3.69s/it] 89%|████████▊ | 461/520 [28:59<03:37, 3.68s/it] {'loss': 1.499, 'grad_norm': 0.0, 'learning_rate': 0.0066867063190933496, 'epoch': 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[launch.py:348:main] Process 832676 exits successfully. +[2025-10-18 01:57:01,833] [INFO] [launch.py:348:main] Process 832674 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.3_2e-1_connector-9.0_1.3_2e-1_ablation_20251018_012245.log +Timestamp: 2025-10-18 01:57:04 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation_20251018_015704.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation_20251018_015704.log new file mode 100644 index 0000000000000000000000000000000000000000..c2f1e8f5f9d5ad976c15f2e6778b1fe5d02e69cb --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation_20251018_015704.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation_20251018_015704.log +Timestamp: 2025-10-18 01:57:04 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:57:06,997] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:09,693] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 01:57:09,694] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 1.5 --temperature_mlp_text 1.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 1.5 --temperature_mlp_vision 1.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 1.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:57:12,302] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:13,356] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 01:57:13,356] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 01:57:13,357] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 01:57:13,357] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 01:57:13,357] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 01:57:13,357] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 01:57:13,357] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 01:57:13,359] [INFO] [launch.py:253:main] process 854702 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,361] [INFO] [launch.py:253:main] process 854703 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,363] [INFO] [launch.py:253:main] process 854704 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,364] [INFO] [launch.py:253:main] process 854705 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,366] [INFO] [launch.py:253:main] process 854706 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,368] [INFO] [launch.py:253:main] process 854707 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,370] [INFO] [launch.py:253:main] process 854708 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 01:57:13,372] [INFO] [launch.py:253:main] process 854709 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.5', '--temperature_mlp_text', '1.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.5', '--temperature_mlp_vision', '1.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 01:57:20,001] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,259] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,312] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,404] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,456] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,462] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,481] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,483] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,486] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 01:57:20,658] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,705] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,865] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,866] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,892] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,897] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 01:57:20,897] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 01:57:20,909] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.5, 'temperature_mlp': 1.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.5, + "temperature_mlp": 1.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. 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+ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854709:856309 [7] NCCL INFO ncclCommInitRank comm 0x5614e3048cf0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854708:856312 [6] NCCL INFO ncclCommInitRank comm 0x556e18652310 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854706:856310 [4] NCCL INFO ncclCommInitRank comm 0x55a572f142f0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854705:856313 [3] NCCL INFO ncclCommInitRank comm 0x55e421e0ab10 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:854704:856311 [2] NCCL INFO ncclCommInitRank comm 0x564abd7a3010 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854707:856308 [5] NCCL INFO ncclCommInitRank comm 0x564b1ba5f5f0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:854703:856307 [1] NCCL INFO ncclCommInitRank comm 0x565022b226b0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xa75c12164244d9c - Init COMPLETE +ywang29-vrdb-test1-worker-0:854702:856306 [0] NCCL INFO ncclCommInitRank comm 0x562387c57bc0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xa75c12164244d9c - Init COMPLETE +[2025-10-18 01:58:05,276] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 01:58:07,000] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 01:58:27,123 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 01:58:27,127 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:000->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO comm 0x7fd52806ac90 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO comm 0x7f952806aa30 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO comm 0x7fd9bc06b050 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO comm 0x7fdea006b710 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO comm 0x7f2a3c06ab80 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO comm 0x7fcd7806ab90 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO comm 0x7f512806ab90 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:854709:861325 [7] NCCL INFO ncclCommInitRank comm 0x7fd52806ac90 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854707:861324 [5] NCCL INFO ncclCommInitRank comm 0x7fdea006b710 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854706:861326 [4] NCCL INFO ncclCommInitRank comm 0x7f2a3c06ab80 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854705:861328 [3] NCCL INFO ncclCommInitRank comm 0x7fcd7806ab90 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854703:861323 [1] NCCL INFO ncclCommInitRank comm 0x7fd9bc06b050 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854702:861322 [0] NCCL INFO ncclCommInitRank comm 0x7f5a1406b880 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854704:861329 [2] NCCL INFO ncclCommInitRank comm 0x7f512806ab90 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE +ywang29-vrdb-test1-worker-0:854708:861327 [6] NCCL INFO ncclCommInitRank comm 0x7f952806aa30 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xa7a3a14d1c0fe8dc - Init COMPLETE + 0%| | 1/520 [00:14<2:02:51, 14.20s/it] {'loss': 2.0453, 'grad_norm': 0.0016138568398658413, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:02:51, 14.20s/it] 0%| | 2/520 [00:17<1:08:45, 7.96s/it] {'loss': 2.0549, 'grad_norm': 0.0017523700778672687, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:08:45, 7.96s/it] 1%| | 3/520 [00:21<51:34, 5.99s/it] {'loss': 2.1899, 'grad_norm': 0.0020044957510095616, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<51:34, 5.99s/it] 1%| | 4/520 [00:25<43:28, 5.05s/it] {'loss': 2.0656, 'grad_norm': 0.0016572145592759905, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<43:28, 5.05s/it] 1%| | 5/520 [00:28<38:56, 4.54s/it] {'loss': 2.2333, 'grad_norm': 0.0018295905659204417, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<38:56, 4.54s/it] 1%| | 6/520 [00:32<36:19, 4.24s/it] {'loss': 1.6754, 'grad_norm': 0.000935296915596094, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:19, 4.24s/it] 1%|▏ | 7/520 [00:36<34:50, 4.08s/it] {'loss': 2.0776, 'grad_norm': 0.0018072996362810336, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<34:50, 4.08s/it] 2%|▏ | 8/520 [00:40<35:36, 4.17s/it] {'loss': 2.0541, 'grad_norm': 0.0015263360028472082, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<35:36, 4.17s/it] 2%|▏ | 9/520 [00:44<35:37, 4.18s/it] {'loss': 2.0596, 'grad_norm': 0.0013705624602067877, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<35:37, 4.18s/it] 2%|▏ | 10/520 [00:48<34:06, 4.01s/it] {'loss': 1.6507, 'grad_norm': 0.0006753204630710508, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<34:06, 4.01s/it] 2%|▏ | 11/520 [00:52<33:36, 3.96s/it] {'loss': 1.5803, 'grad_norm': 0.00029283410315812474, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:52<33:36, 3.96s/it] 2%|▏ | 12/520 [00:55<33:05, 3.91s/it] {'loss': 1.4305, 'grad_norm': 0.0001939727408573308, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<33:05, 3.91s/it][2025-10-18 01:59:32,931] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:00<34:23, 4.07s/it] {'loss': 1.5353, 'grad_norm': 0.00020899666278499562, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:00<34:23, 4.07s/it] 3%|▎ | 14/520 [01:04<33:36, 3.99s/it] {'loss': 1.5477, 'grad_norm': 0.00017090300908303527, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:04<33:36, 3.99s/it] 3%|▎ | 15/520 [01:07<33:02, 3.92s/it] {'loss': 1.4571, 'grad_norm': 0.00013845211394659015, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:07<33:02, 3.92s/it] 3%|▎ | 16/520 [01:11<32:31, 3.87s/it] {'loss': 1.4222, 'grad_norm': 0.00013693798169942342, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:11<32:31, 3.87s/it] 3%|▎ | 17/520 [01:15<32:11, 3.84s/it] {'loss': 1.5765, 'grad_norm': 0.00015022293186769324, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:15<32:11, 3.84s/it] 3%|▎ | 18/520 [01:19<31:57, 3.82s/it] {'loss': 1.4384, 'grad_norm': 0.0001509660583510394, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:19<31:57, 3.82s/it] 4%|▎ | 19/520 [01:22<31:45, 3.80s/it] {'loss': 1.4104, 'grad_norm': 0.0001347440358195172, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:22<31:45, 3.80s/it] 4%|▍ | 20/520 [01:26<31:16, 3.75s/it] {'loss': 1.3971, 'grad_norm': 0.00016432059419924737, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:26<31:16, 3.75s/it] 4%|▍ | 21/520 [01:30<31:02, 3.73s/it] {'loss': 1.3985, 'grad_norm': 0.00015896582566010561, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:30<31:02, 3.73s/it] 4%|▍ | 22/520 [01:33<30:46, 3.71s/it] {'loss': 1.5223, 'grad_norm': 0.00015685409005090857, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:33<30:46, 3.71s/it] 4%|▍ | 23/520 [01:37<30:32, 3.69s/it] {'loss': 1.4591, 'grad_norm': 0.00014901891875161824, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:37<30:32, 3.69s/it] 5%|▍ | 24/520 [01:41<30:25, 3.68s/it] {'loss': 1.374, 'grad_norm': 0.00015181368582579613, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:41<30:25, 3.68s/it] 5%|▍ | 25/520 [01:44<30:13, 3.66s/it] {'loss': 1.4924, 'grad_norm': 0.00019644918428222147, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:44<30:13, 3.66s/it] 5%|▌ | 26/520 [01:48<30:08, 3.66s/it] {'loss': 1.4032, 'grad_norm': 0.00015205932415454174, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<30:08, 3.66s/it] 5%|▌ | 27/520 [01:52<29:58, 3.65s/it] {'loss': 1.3388, 'grad_norm': 0.0001607624606670631, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:52<29:58, 3.65s/it] 5%|▌ | 28/520 [01:55<29:58, 3.65s/it] {'loss': 1.3726, 'grad_norm': 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0.00028570930210835676, 'learning_rate': 0.1970596567453391, 'epoch': 0.11} + 11%|█ | 55/520 [03:34<28:06, 3.63s/it] 11%|█ | 56/520 [03:37<28:01, 3.62s/it] {'loss': 1.4273, 'grad_norm': 0.00028402669118458536, 'learning_rate': 0.1969077286229078, 'epoch': 0.11} + 11%|█ | 56/520 [03:37<28:01, 3.62s/it] 11%|█ | 57/520 [03:41<27:55, 3.62s/it] {'loss': 1.308, 'grad_norm': 0.0003244655548291847, 'learning_rate': 0.19675203523431964, 'epoch': 0.11} + 11%|█ | 57/520 [03:41<27:55, 3.62s/it] 11%|█ | 58/520 [03:45<27:52, 3.62s/it] {'loss': 1.4335, 'grad_norm': 0.0002462402262190529, 'learning_rate': 0.19659258262890683, 'epoch': 0.11} + 11%|█ | 58/520 [03:45<27:52, 3.62s/it] 11%|█▏ | 59/520 [03:48<27:53, 3.63s/it] {'loss': 1.2476, 'grad_norm': 0.00026382348296728167, 'learning_rate': 0.19642937700206278, 'epoch': 0.11} + 11%|█▏ | 59/520 [03:48<27:53, 3.63s/it] 12%|█▏ | 60/520 [03:52<27:52, 3.64s/it] {'loss': 1.3504, 'grad_norm': 0.00027450089285634426, 'learning_rate': 0.19626242469500121, 'epoch': 0.12} + 12%|█▏ | 60/520 [03:52<27:52, 3.64s/it] 12%|█▏ | 61/520 [03:56<27:52, 3.64s/it] {'loss': 1.3197, 'grad_norm': 0.00031870783895527374, 'learning_rate': 0.19609173219450998, 'epoch': 0.12} + 12%|█▏ | 61/520 [03:56<27:52, 3.64s/it] 12%|█▏ | 62/520 [03:59<27:47, 3.64s/it] {'loss': 1.3339, 'grad_norm': 0.0003281422018744392, 'learning_rate': 0.19591730613269878, 'epoch': 0.12} + 12%|█▏ | 62/520 [03:59<27:47, 3.64s/it] 12%|█▏ | 63/520 [04:03<27:48, 3.65s/it] {'loss': 1.3363, 'grad_norm': 0.0003411128008594187, 'learning_rate': 0.19573915328674182, 'epoch': 0.12} + 12%|█▏ | 63/520 [04:03<27:48, 3.65s/it] 12%|█▏ | 64/520 [04:06<27:41, 3.64s/it] {'loss': 1.3547, 'grad_norm': 0.00031389068605450313, 'learning_rate': 0.1955572805786141, 'epoch': 0.12} + 12%|█▏ | 64/520 [04:06<27:41, 3.64s/it] 12%|█▎ | 65/520 [04:10<27:41, 3.65s/it] {'loss': 1.3613, 'grad_norm': 0.00033401660403705905, 'learning_rate': 0.1953716950748227, 'epoch': 0.12} + 12%|█▎ | 65/520 [04:10<27:41, 3.65s/it] 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{'loss': 1.216, 'grad_norm': 0.0005108384492070592, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:16<10:53, 3.67s/it] 66%|██████▌ | 343/520 [21:19<10:49, 3.67s/it] {'loss': 1.1533, 'grad_norm': 0.0003597323111084344, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:19<10:49, 3.67s/it] 66%|██████▌ | 344/520 [21:23<10:46, 3.68s/it] {'loss': 1.1641, 'grad_norm': 0.0004289077158573783, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:23<10:46, 3.68s/it] 66%|██████▋ | 345/520 [21:27<10:44, 3.68s/it] {'loss': 1.2617, 'grad_norm': 0.0004717762116028275, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:27<10:44, 3.68s/it] 67%|██████▋ | 346/520 [21:31<10:54, 3.76s/it] {'loss': 1.1767, 'grad_norm': 0.0004247322317189884, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:31<10:54, 3.76s/it] 67%|██████▋ | 347/520 [21:34<10:57, 3.80s/it] {'loss': 1.1796, 'grad_norm': 0.00041457480528809296, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:34<10:57, 3.80s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:38<11:00, 3.84s/it] {'loss': 1.1372, 'grad_norm': 0.0005344689973837913, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:38<11:00, 3.84s/it] 67%|██████▋ | 349/520 [21:42<10:59, 3.86s/it] {'loss': 1.1702, 'grad_norm': 0.00046856388321492846, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:42<10:59, 3.86s/it] 67%|██████▋ | 350/520 [21:46<10:57, 3.87s/it] {'loss': 1.2176, 'grad_norm': 0.0004809408660155632, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:46<10:57, 3.87s/it] 68%|██████▊ | 351/520 [21:50<10:48, 3.84s/it] {'loss': 1.1303, 'grad_norm': 0.00044601191878469384, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:50<10:48, 3.84s/it] 68%|██████▊ | 352/520 [21:54<10:36, 3.79s/it] {'loss': 1.2402, 'grad_norm': 0.0004232308843817825, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:54<10:36, 3.79s/it] 68%|██████▊ | 353/520 [21:57<10:29, 3.77s/it] {'loss': 1.1547, 'grad_norm': 0.00037019429602448673, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:57<10:29, 3.77s/it] 68%|██████▊ | 354/520 [22:01<10:21, 3.74s/it] {'loss': 1.244, 'grad_norm': 0.000407424784982228, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:01<10:21, 3.74s/it] 68%|██████▊ | 355/520 [22:05<10:15, 3.73s/it] {'loss': 1.1948, 'grad_norm': 0.0004630133994343312, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:05<10:15, 3.73s/it] 68%|██████▊ | 356/520 [22:08<10:09, 3.72s/it] {'loss': 1.1946, 'grad_norm': 0.00047008305136242266, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:08<10:09, 3.72s/it] 69%|██████▊ | 357/520 [22:12<10:03, 3.70s/it] {'loss': 1.2284, 'grad_norm': 0.00043337150854582654, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:12<10:03, 3.70s/it] 69%|██████▉ | 358/520 [22:16<09:59, 3.70s/it] {'loss': 1.1587, 'grad_norm': 0.0004572181108229964, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:16<09:59, 3.70s/it] 69%|██████▉ | 359/520 [22:19<09:54, 3.69s/it] {'loss': 1.1904, 'grad_norm': 0.0004514440348722102, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:19<09:54, 3.69s/it] 69%|██████▉ | 360/520 [22:23<09:49, 3.69s/it] {'loss': 1.1904, 'grad_norm': 0.0004267828274694092, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:23<09:49, 3.69s/it] 69%|██████▉ | 361/520 [22:27<09:44, 3.68s/it] {'loss': 1.2149, 'grad_norm': 0.000390389382479135, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:27<09:44, 3.68s/it] 70%|██████▉ | 362/520 [22:30<09:39, 3.67s/it] {'loss': 1.2008, 'grad_norm': 0.0005006340407784605, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:30<09:39, 3.67s/it] 70%|██████▉ | 363/520 [22:34<09:38, 3.68s/it] {'loss': 1.2347, 'grad_norm': 0.0004462822166893205, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:34<09:38, 3.68s/it] 70%|███████ | 364/520 [22:38<09:35, 3.69s/it] {'loss': 1.2214, 'grad_norm': 0.00044095098837799887, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:38<09:35, 3.69s/it] 70%|███████ | 365/520 [22:42<09:29, 3.67s/it] {'loss': 1.2829, 'grad_norm': 0.00046219074633117407, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:42<09:29, 3.67s/it] 70%|███████ | 366/520 [22:45<09:25, 3.67s/it] {'loss': 1.2582, 'grad_norm': 0.0004467320082854869, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:45<09:25, 3.67s/it] 71%|███████ | 367/520 [22:49<09:24, 3.69s/it] {'loss': 1.2483, 'grad_norm': 0.000465449175273824, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:49<09:24, 3.69s/it] 71%|███████ | 368/520 [22:53<09:28, 3.74s/it] {'loss': 1.0996, 'grad_norm': 0.00046643437143644844, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:53<09:28, 3.74s/it] 71%|███████ | 369/520 [22:57<09:32, 3.79s/it] {'loss': 1.1908, 'grad_norm': 0.0004027999578431983, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:57<09:32, 3.79s/it] 71%|███████ | 370/520 [23:01<09:30, 3.80s/it] {'loss': 1.1639, 'grad_norm': 0.00043858996826733683, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:01<09:30, 3.80s/it] 71%|███████▏ | 371/520 [23:04<09:20, 3.76s/it] {'loss': 1.1471, 'grad_norm': 0.00048062291357234694, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:04<09:20, 3.76s/it] 72%|███████▏ | 372/520 [23:08<09:13, 3.74s/it] {'loss': 1.2524, 'grad_norm': 0.00038801418118441627, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:08<09:13, 3.74s/it] 72%|███████▏ | 373/520 [23:12<09:13, 3.76s/it] {'loss': 1.145, 'grad_norm': 0.00047808642026944974, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:12<09:13, 3.76s/it] 72%|███████▏ | 374/520 [23:16<09:11, 3.78s/it] {'loss': 1.2463, 'grad_norm': 0.0004781995106732123, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:16<09:11, 3.78s/it] 72%|███████▏ | 375/520 [23:19<09:09, 3.79s/it] {'loss': 1.1643, 'grad_norm': 0.0004694863038249616, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:19<09:09, 3.79s/it] 72%|███████▏ | 376/520 [23:23<09:05, 3.79s/it] {'loss': 1.2709, 'grad_norm': 0.0004391143112296753, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:23<09:05, 3.79s/it] 72%|███████▎ | 377/520 [23:27<09:01, 3.79s/it] {'loss': 1.202, 'grad_norm': 0.0004943536421857693, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:27<09:01, 3.79s/it] 73%|███████▎ | 378/520 [23:31<08:56, 3.78s/it] {'loss': 1.2663, 'grad_norm': 0.0004283949566895077, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:31<08:56, 3.78s/it] 73%|███████▎ | 379/520 [23:34<08:49, 3.76s/it] {'loss': 1.2252, 'grad_norm': 0.00042038396151800587, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:34<08:49, 3.76s/it] 73%|███████▎ | 380/520 [23:38<08:40, 3.72s/it] {'loss': 1.236, 'grad_norm': 0.0004430830430583783, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:38<08:40, 3.72s/it] 73%|███████▎ | 381/520 [23:42<08:35, 3.71s/it] {'loss': 1.2411, 'grad_norm': 0.0004358162657582347, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:42<08:35, 3.71s/it] 73%|███████▎ | 382/520 [23:45<08:34, 3.73s/it] {'loss': 1.2101, 'grad_norm': 0.0004052617246526152, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:45<08:34, 3.73s/it] 74%|███████▎ | 383/520 [23:49<08:30, 3.73s/it] {'loss': 1.0842, 'grad_norm': 0.0005037874278197328, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:49<08:30, 3.73s/it] 74%|███████▍ | 384/520 [23:53<08:25, 3.71s/it] {'loss': 1.2188, 'grad_norm': 0.0003754251262686486, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:53<08:25, 3.71s/it] 74%|███████▍ | 385/520 [23:57<08:19, 3.70s/it] {'loss': 1.2339, 'grad_norm': 0.00043058500105068345, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:57<08:19, 3.70s/it] 74%|███████▍ | 386/520 [24:00<08:14, 3.69s/it] {'loss': 1.1747, 'grad_norm': 0.0003928416180147897, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:00<08:14, 3.69s/it] 74%|███████▍ | 387/520 [24:04<08:10, 3.68s/it] {'loss': 1.2501, 'grad_norm': 0.00042792482441803714, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:04<08:10, 3.68s/it] 75%|███████▍ | 388/520 [24:08<08:05, 3.68s/it] {'loss': 1.1405, 'grad_norm': 0.00044188667279813996, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:08<08:05, 3.68s/it] 75%|███████▍ | 389/520 [24:11<08:01, 3.67s/it] {'loss': 1.1909, 'grad_norm': 0.0005954708663503922, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:11<08:01, 3.67s/it] 75%|███████▌ | 390/520 [24:15<07:57, 3.67s/it] {'loss': 1.2559, 'grad_norm': 0.0004288995994294231, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:15<07:57, 3.67s/it] 75%|███████▌ | 391/520 [24:19<07:54, 3.68s/it] {'loss': 1.3107, 'grad_norm': 0.00045194791315574667, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:19<07:54, 3.68s/it] 75%|███████▌ | 392/520 [24:22<07:50, 3.68s/it] {'loss': 1.1399, 'grad_norm': 0.00044457399955765705, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:22<07:50, 3.68s/it] 76%|███████▌ | 393/520 [24:26<07:46, 3.67s/it] {'loss': 1.118, 'grad_norm': 0.0003780308159651078, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:26<07:46, 3.67s/it] 76%|███████▌ | 394/520 [24:30<07:41, 3.67s/it] {'loss': 1.2149, 'grad_norm': 0.00047073466772441227, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:30<07:41, 3.67s/it] 76%|███████▌ | 395/520 [24:33<07:38, 3.67s/it] {'loss': 1.1829, 'grad_norm': 0.0004820927317582961, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:33<07:38, 3.67s/it] 76%|███████▌ | 396/520 [24:37<07:33, 3.66s/it] {'loss': 1.2497, 'grad_norm': 0.0004704317923202109, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:37<07:33, 3.66s/it] 76%|███████▋ | 397/520 [24:41<07:31, 3.67s/it] {'loss': 1.2253, 'grad_norm': 0.0004415006899357719, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:41<07:31, 3.67s/it] 77%|███████▋ | 398/520 [24:44<07:28, 3.68s/it] {'loss': 1.2153, 'grad_norm': 0.0004801635860022106, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:44<07:28, 3.68s/it] 77%|███████▋ | 399/520 [24:48<07:25, 3.68s/it] {'loss': 1.1449, 'grad_norm': 0.00042203474767373906, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:48<07:25, 3.68s/it] 77%|███████▋ | 400/520 [24:52<07:21, 3.68s/it] {'loss': 1.1824, 'grad_norm': 0.00040382825492693336, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:52<07:21, 3.68s/it] 77%|███████▋ | 401/520 [24:55<07:17, 3.67s/it] {'loss': 1.0607, 'grad_norm': 0.0004871966344808475, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:55<07:17, 3.67s/it] 77%|███████▋ | 402/520 [24:59<07:11, 3.66s/it] {'loss': 1.1947, 'grad_norm': 0.0004704048861719937, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:59<07:11, 3.66s/it] 78%|███████▊ | 403/520 [25:03<07:09, 3.67s/it] {'loss': 1.2143, 'grad_norm': 0.0004848400518742812, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:03<07:09, 3.67s/it] 78%|███████▊ | 404/520 [25:06<07:04, 3.66s/it] {'loss': 1.1311, 'grad_norm': 0.0005378641808331823, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:06<07:04, 3.66s/it] 78%|███████▊ | 405/520 [25:10<07:03, 3.68s/it] {'loss': 1.1635, 'grad_norm': 0.0004265810427440692, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:10<07:03, 3.68s/it] 78%|███████▊ | 406/520 [25:14<07:00, 3.69s/it] {'loss': 1.0919, 'grad_norm': 0.0005265943898931305, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:14<07:00, 3.69s/it] 78%|███████▊ | 407/520 [25:17<06:55, 3.68s/it] {'loss': 1.2868, 'grad_norm': 0.00045696423020082495, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:17<06:55, 3.68s/it] 78%|███████▊ | 408/520 [25:21<06:52, 3.68s/it] {'loss': 1.2097, 'grad_norm': 0.0005093991239759515, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:21<06:52, 3.68s/it] 79%|███████▊ | 409/520 [25:25<06:48, 3.68s/it] {'loss': 1.3282, 'grad_norm': 0.00047650374559638333, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:25<06:48, 3.68s/it] 79%|███████▉ | 410/520 [25:28<06:43, 3.67s/it] {'loss': 1.0642, 'grad_norm': 0.00046714544535239713, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:28<06:43, 3.67s/it] 79%|███████▉ | 411/520 [25:32<06:38, 3.66s/it] {'loss': 1.3033, 'grad_norm': 0.00048222166827165694, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:32<06:38, 3.66s/it] 79%|███████▉ | 412/520 [25:36<06:34, 3.66s/it] {'loss': 1.2178, 'grad_norm': 0.00044290580160595837, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:36<06:34, 3.66s/it] 79%|███████▉ | 413/520 [25:39<06:31, 3.66s/it] {'loss': 1.1859, 'grad_norm': 0.0004258009405578501, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:39<06:31, 3.66s/it] 80%|███████▉ | 414/520 [25:43<06:28, 3.66s/it] {'loss': 0.9902, 'grad_norm': 0.00037390066288916585, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:43<06:28, 3.66s/it] 80%|███████▉ | 415/520 [25:47<06:24, 3.66s/it] {'loss': 1.1968, 'grad_norm': 0.0004327237329477778, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:47<06:24, 3.66s/it] 80%|████████ | 416/520 [25:50<06:21, 3.67s/it] {'loss': 1.0992, 'grad_norm': 0.0005024746737142635, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:50<06:21, 3.67s/it] 80%|████████ | 417/520 [25:54<06:20, 3.70s/it] {'loss': 1.2584, 'grad_norm': 0.000445351665149149, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:54<06:20, 3.70s/it] 80%|████████ | 418/520 [25:58<06:23, 3.76s/it] {'loss': 1.2569, 'grad_norm': 0.0004211949069416564, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:58<06:23, 3.76s/it] 81%|████████ | 419/520 [26:02<06:23, 3.80s/it] {'loss': 1.2548, 'grad_norm': 0.0004923000254623563, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:02<06:23, 3.80s/it] 81%|████████ | 420/520 [26:06<06:20, 3.80s/it] {'loss': 1.1416, 'grad_norm': 0.0004742915157792383, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:06<06:20, 3.80s/it] 81%|████████ | 421/520 [26:09<06:13, 3.77s/it] {'loss': 1.078, 'grad_norm': 0.00048288781153393514, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:09<06:13, 3.77s/it] 81%|████████ | 422/520 [26:13<06:06, 3.74s/it] {'loss': 1.2045, 'grad_norm': 0.00048067046508975587, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:13<06:06, 3.74s/it] 81%|████████▏ | 423/520 [26:17<06:01, 3.73s/it] {'loss': 1.1718, 'grad_norm': 0.0004941235308281212, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:17<06:01, 3.73s/it] 82%|████████▏ | 424/520 [26:20<05:57, 3.73s/it] {'loss': 1.2586, 'grad_norm': 0.0003984911618683602, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:20<05:57, 3.73s/it] 82%|████████▏ | 425/520 [26:24<05:51, 3.70s/it] {'loss': 1.1805, 'grad_norm': 0.0004451402411823143, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:24<05:51, 3.70s/it] 82%|████████▏ | 426/520 [26:28<05:47, 3.69s/it] {'loss': 1.2288, 'grad_norm': 0.0005985695835774873, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:28<05:47, 3.69s/it] 82%|████████▏ | 427/520 [26:31<05:42, 3.68s/it] {'loss': 1.111, 'grad_norm': 0.0004364714594932652, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:31<05:42, 3.68s/it] 82%|████████▏ | 428/520 [26:35<05:38, 3.67s/it] {'loss': 1.1109, 'grad_norm': 0.0004977500406712303, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:35<05:38, 3.67s/it] 82%|████████▎ | 429/520 [26:39<05:34, 3.67s/it] {'loss': 1.2161, 'grad_norm': 0.0004617144505751104, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:39<05:34, 3.67s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:43<05:35, 3.73s/it] {'loss': 1.2118, 'grad_norm': 0.00043921207768232034, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:43<05:35, 3.73s/it] 83%|████████▎ | 431/520 [26:46<05:35, 3.77s/it] {'loss': 1.1506, 'grad_norm': 0.0004268874905453158, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:46<05:35, 3.77s/it] 83%|████████▎ | 432/520 [26:50<05:36, 3.82s/it] {'loss': 1.119, 'grad_norm': 0.00048477855073061264, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:50<05:36, 3.82s/it] 83%|████████▎ | 433/520 [26:54<05:33, 3.84s/it] {'loss': 1.2532, 'grad_norm': 0.0004538395933921179, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:54<05:33, 3.84s/it] 83%|████████▎ | 434/520 [26:58<05:31, 3.86s/it] {'loss': 1.0133, 'grad_norm': 0.00047942235792997035, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:58<05:31, 3.86s/it] 84%|████████▎ | 435/520 [27:02<05:29, 3.87s/it] {'loss': 1.2864, 'grad_norm': 0.0004913387414749264, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:02<05:29, 3.87s/it] 84%|████████▍ | 436/520 [27:06<05:24, 3.87s/it] {'loss': 1.0951, 'grad_norm': 0.0004878857601877584, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:06<05:24, 3.87s/it] 84%|████████▍ | 437/520 [27:10<05:21, 3.87s/it] {'loss': 1.3073, 'grad_norm': 0.00046291188899920583, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:10<05:21, 3.87s/it] 84%|████████▍ | 438/520 [27:14<05:18, 3.88s/it] {'loss': 1.1253, 'grad_norm': 0.0004758994831872903, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:14<05:18, 3.88s/it] 84%|████████▍ | 439/520 [27:18<05:12, 3.86s/it] {'loss': 1.1379, 'grad_norm': 0.0003617370408905964, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:18<05:12, 3.86s/it] 85%|████████▍ | 440/520 [27:21<05:03, 3.80s/it] {'loss': 1.1646, 'grad_norm': 0.00048133886484780534, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:21<05:03, 3.80s/it] 85%|████████▍ | 441/520 [27:25<04:57, 3.77s/it] {'loss': 1.1466, 'grad_norm': 0.00043069967358476006, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:25<04:57, 3.77s/it] 85%|████████▌ | 442/520 [27:29<04:50, 3.73s/it] {'loss': 1.2267, 'grad_norm': 0.0005149851208316175, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:29<04:50, 3.73s/it] 85%|████████▌ | 443/520 [27:32<04:45, 3.71s/it] {'loss': 1.2307, 'grad_norm': 0.00044184492394703536, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:32<04:45, 3.71s/it] 85%|████████▌ | 444/520 [27:36<04:41, 3.70s/it] {'loss': 1.1976, 'grad_norm': 0.00041023023224851977, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:36<04:41, 3.70s/it] 86%|████████▌ | 445/520 [27:40<04:36, 3.68s/it] {'loss': 1.1226, 'grad_norm': 0.0004328300183025025, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:40<04:36, 3.68s/it] 86%|████████▌ | 446/520 [27:43<04:33, 3.69s/it] {'loss': 1.228, 'grad_norm': 0.0003969015232500921, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:43<04:33, 3.69s/it] 86%|████████▌ | 447/520 [27:47<04:28, 3.68s/it] {'loss': 1.1934, 'grad_norm': 0.00044733403518917706, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:47<04:28, 3.68s/it] 86%|████████▌ | 448/520 [27:51<04:24, 3.68s/it] {'loss': 1.1957, 'grad_norm': 0.000518018338308074, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:51<04:24, 3.68s/it] 86%|████████▋ | 449/520 [27:54<04:20, 3.67s/it] {'loss': 1.1882, 'grad_norm': 0.00043676751732357494, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:54<04:20, 3.67s/it] 87%|████████▋ | 450/520 [27:58<04:15, 3.66s/it] {'loss': 1.2212, 'grad_norm': 0.000472129579145812, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:58<04:15, 3.66s/it] 87%|████████▋ | 451/520 [28:01<04:12, 3.66s/it] {'loss': 1.2212, 'grad_norm': 0.00046623237406498197, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:01<04:12, 3.66s/it] 87%|████████▋ | 452/520 [28:05<04:08, 3.65s/it] {'loss': 1.2352, 'grad_norm': 0.0004204014717661072, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:05<04:08, 3.65s/it] 87%|████████▋ | 453/520 [28:09<04:04, 3.65s/it] {'loss': 1.2157, 'grad_norm': 0.00043362884894585825, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:09<04:04, 3.65s/it] 87%|████████▋ | 454/520 [28:12<04:00, 3.65s/it] {'loss': 1.1337, 'grad_norm': 0.0004552986098152805, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:12<04:00, 3.65s/it] 88%|████████▊ | 455/520 [28:16<03:57, 3.65s/it] {'loss': 1.2687, 'grad_norm': 0.0004577964453447138, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:16<03:57, 3.65s/it] 88%|████████▊ | 456/520 [28:20<03:53, 3.65s/it] {'loss': 1.2115, 'grad_norm': 0.00047495661829024654, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:20<03:53, 3.65s/it] 88%|████████▊ | 457/520 [28:23<03:50, 3.65s/it] {'loss': 1.0821, 'grad_norm': 0.00039321219886202373, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:23<03:50, 3.65s/it] 88%|████████▊ | 458/520 [28:27<03:46, 3.66s/it] {'loss': 1.3207, 'grad_norm': 0.00048384572754660017, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:27<03:46, 3.66s/it] 88%|████████▊ | 459/520 [28:31<03:44, 3.69s/it] {'loss': 1.2509, 'grad_norm': 0.0004492374526250833, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:31<03:44, 3.69s/it] 88%|████████▊ | 460/520 [28:34<03:41, 3.69s/it] {'loss': 1.1443, 'grad_norm': 0.0004653866693957693, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:34<03:41, 3.69s/it] 89%|████████▊ | 461/520 [28:38<03:37, 3.68s/it] {'loss': 1.1677, 'grad_norm': 0.0003172535069417671, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:38<03:37, 3.68s/it] 89%|████████▉ | 462/520 [28:42<03:33, 3.68s/it] {'loss': 1.2734, 'grad_norm': 0.0004239738076585924, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:42<03:33, 3.68s/it] 89%|████████▉ | 463/520 [28:45<03:29, 3.67s/it] {'loss': 1.1254, 'grad_norm': 0.0004789486211406575, 'learning_rate': 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qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.5_2e-1_connector-9.0_1.5_2e-1_ablation_20251018_015704.log +Timestamp: 2025-10-18 02:31:03 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation_20251018_023103.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation_20251018_023103.log new file mode 100644 index 0000000000000000000000000000000000000000..84cf3ee155f82258febcddf6b4d2a8b6bc09a784 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation_20251018_023103.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation_20251018_023103.log +Timestamp: 2025-10-18 02:31:03 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 02:31:05,805] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:08,747] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 02:31:08,748] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 1.7 --temperature_mlp_text 1.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 1.7 --temperature_mlp_vision 1.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 1.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 02:31:11,340] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:12,409] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 02:31:12,409] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 02:31:12,409] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 02:31:12,409] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 02:31:12,409] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 02:31:12,409] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 02:31:12,409] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 02:31:12,411] [INFO] [launch.py:253:main] process 876688 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,413] [INFO] [launch.py:253:main] process 876689 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,415] [INFO] [launch.py:253:main] process 876690 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,417] [INFO] [launch.py:253:main] process 876691 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,419] [INFO] [launch.py:253:main] process 876692 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,421] [INFO] [launch.py:253:main] process 876693 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,423] [INFO] [launch.py:253:main] process 876694 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 02:31:12,424] [INFO] [launch.py:253:main] process 876695 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.7', '--temperature_mlp_text', '1.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.7', '--temperature_mlp_vision', '1.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 02:31:19,058] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,087] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,326] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,423] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,424] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,425] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,425] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,426] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 02:31:19,481] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,516] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,743] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,832] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,832] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,832] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,833] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 02:31:19,833] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 02:31:19,834] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.7, 'temperature_mlp': 1.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.7, + "temperature_mlp": 1.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO comm 0x55966f03fcb0 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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+ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer 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channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876695:878310 [7] NCCL INFO ncclCommInitRank comm 0x557fef6d3d80 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876693:878329 [5] NCCL INFO ncclCommInitRank comm 0x55966f03fcb0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876692:878308 [4] NCCL INFO ncclCommInitRank comm 0x55e690d8f270 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876694:878307 [6] NCCL INFO ncclCommInitRank comm 0x55887659b640 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876691:878328 [3] NCCL INFO ncclCommInitRank comm 0x564f247c1730 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:876688:878306 [0] NCCL INFO ncclCommInitRank comm 0x55ce5f9040f0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876690:878311 [2] NCCL INFO ncclCommInitRank comm 0x555cf7768420 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xbc9b3f58be78e215 - Init COMPLETE +ywang29-vrdb-test1-worker-0:876689:878309 [1] NCCL INFO ncclCommInitRank comm 0x5561f9493720 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xbc9b3f58be78e215 - Init COMPLETE +[2025-10-18 02:32:04,318] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 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'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 02:32:06,059] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 02:32:27,609 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 02:32:27,616 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:004->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:876692:883356 [4] NCCL INFO ncclCommInitRank comm 0x7fa29c06add0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876695:883358 [7] NCCL INFO ncclCommInitRank comm 0x7f092c06a540 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876691:883359 [3] NCCL INFO ncclCommInitRank comm 0x7fc5f006b280 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876688:883355 [0] NCCL INFO ncclCommInitRank comm 0x7f519006b670 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876694:883362 [6] NCCL INFO ncclCommInitRank comm 0x7fd79c06a500 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876690:883357 [2] NCCL INFO ncclCommInitRank comm 0x7fd27406b000 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876693:883361 [5] NCCL INFO ncclCommInitRank comm 0x7f9ad806a8a0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xcf131f25c2e3019f - Init COMPLETE +ywang29-vrdb-test1-worker-0:876689:883360 [1] NCCL INFO ncclCommInitRank comm 0x7f6b0006a9c0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xcf131f25c2e3019f - Init COMPLETE + 0%| | 1/520 [00:13<2:00:33, 13.94s/it] {'loss': 2.0453, 'grad_norm': 0.0014245150557994127, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:13<2:00:33, 13.94s/it] 0%| | 2/520 [00:17<1:07:52, 7.86s/it] {'loss': 2.0549, 'grad_norm': 0.0015462046421839805, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:07:52, 7.86s/it] 1%| | 3/520 [00:21<50:59, 5.92s/it] {'loss': 2.1899, 'grad_norm': 0.0017686398732226492, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<50:59, 5.92s/it] 1%| | 4/520 [00:24<43:02, 5.00s/it] {'loss': 2.0656, 'grad_norm': 0.0014621791751581964, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:24<43:02, 5.00s/it] 1%| | 5/520 [00:28<38:40, 4.51s/it] {'loss': 2.2333, 'grad_norm': 0.0016146980290433974, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<38:40, 4.51s/it] 1%| | 6/520 [00:32<36:02, 4.21s/it] {'loss': 1.6754, 'grad_norm': 0.0008257198268312136, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:02, 4.21s/it] 1%|▏ | 7/520 [00:35<34:14, 4.00s/it] {'loss': 2.0776, 'grad_norm': 0.0015955144633034383, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:35<34:14, 4.00s/it] 2%|▏ | 8/520 [00:39<34:40, 4.06s/it] {'loss': 2.0143, 'grad_norm': 0.0013216230550906157, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:39<34:40, 4.06s/it] 2%|▏ | 9/520 [00:43<34:42, 4.08s/it] {'loss': 1.7913, 'grad_norm': 0.0011181954734635886, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:43<34:42, 4.08s/it] 2%|▏ | 10/520 [00:47<33:25, 3.93s/it] {'loss': 1.6007, 'grad_norm': 0.0007921172192076798, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:47<33:25, 3.93s/it] 2%|▏ | 11/520 [00:51<32:46, 3.86s/it] {'loss': 1.5849, 'grad_norm': 0.0004925694685126238, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:51<32:46, 3.86s/it] 2%|▏ | 12/520 [00:54<32:01, 3.78s/it] {'loss': 1.4478, 'grad_norm': 0.0003948959280246652, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:54<32:01, 3.78s/it][2025-10-18 02:33:32,313] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:59<33:15, 3.94s/it] {'loss': 1.5594, 'grad_norm': 0.0003622045858944779, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:59<33:15, 3.94s/it] 3%|▎ | 14/520 [01:02<32:21, 3.84s/it] {'loss': 1.5726, 'grad_norm': 0.0003331581237185161, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:02<32:21, 3.84s/it] 3%|▎ | 15/520 [01:06<31:40, 3.76s/it] {'loss': 1.4658, 'grad_norm': 0.00021774979046464664, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:06<31:40, 3.76s/it] 3%|▎ | 16/520 [01:09<31:09, 3.71s/it] {'loss': 1.4246, 'grad_norm': 0.0002257306621368813, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:09<31:09, 3.71s/it] 3%|▎ | 17/520 [01:13<30:54, 3.69s/it] {'loss': 1.5781, 'grad_norm': 0.0002663634663118201, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:13<30:54, 3.69s/it] 3%|▎ | 18/520 [01:17<30:39, 3.67s/it] {'loss': 1.4323, 'grad_norm': 0.0002656256593444784, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:17<30:39, 3.67s/it] 4%|▎ | 19/520 [01:20<30:28, 3.65s/it] {'loss': 1.407, 'grad_norm': 0.00026284502013634164, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:20<30:28, 3.65s/it] 4%|▍ | 20/520 [01:24<30:17, 3.63s/it] {'loss': 1.394, 'grad_norm': 0.0002770175893316709, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:24<30:17, 3.63s/it] 4%|▍ | 21/520 [01:27<30:13, 3.63s/it] {'loss': 1.3847, 'grad_norm': 0.00026151566632262254, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:27<30:13, 3.63s/it] 4%|▍ | 22/520 [01:31<30:06, 3.63s/it] {'loss': 1.5191, 'grad_norm': 0.00032605436431622095, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:31<30:06, 3.63s/it] 4%|▍ | 23/520 [01:35<30:01, 3.62s/it] {'loss': 1.457, 'grad_norm': 0.0002807326934668402, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:35<30:01, 3.62s/it] 5%|▍ | 24/520 [01:38<29:54, 3.62s/it] {'loss': 1.3679, 'grad_norm': 0.00028422612695425353, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:38<29:54, 3.62s/it] 5%|▍ | 25/520 [01:42<29:51, 3.62s/it] {'loss': 1.4726, 'grad_norm': 0.00028140364768220057, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:42<29:51, 3.62s/it] 5%|▌ | 26/520 [01:46<29:50, 3.62s/it] {'loss': 1.3865, 'grad_norm': 0.00025049965345961823, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:46<29:50, 3.62s/it] 5%|▌ | 27/520 [01:49<30:00, 3.65s/it] {'loss': 1.3192, 'grad_norm': 0.0002501070422802861, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:49<30:00, 3.65s/it] 5%|▌ | 28/520 [01:53<30:21, 3.70s/it] {'loss': 1.3493, 'grad_norm': 0.0002562209871993272, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:53<30:21, 3.70s/it] 6%|▌ | 29/520 [01:57<30:35, 3.74s/it] {'loss': 1.3613, 'grad_norm': 0.0002607976602470491, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:57<30:35, 3.74s/it] 6%|▌ | 30/520 [02:01<30:43, 3.76s/it] {'loss': 1.4162, 'grad_norm': 0.00024170317384575196, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:01<30:43, 3.76s/it] 6%|▌ | 31/520 [02:05<30:48, 3.78s/it] {'loss': 1.3185, 'grad_norm': 0.00021698480595701001, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:05<30:48, 3.78s/it] 6%|▌ | 32/520 [02:08<30:52, 3.80s/it] {'loss': 1.2311, 'grad_norm': 0.00025331531923508373, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:08<30:52, 3.80s/it] 6%|▋ | 33/520 [02:12<30:50, 3.80s/it] {'loss': 1.3107, 'grad_norm': 0.0002613223259895902, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 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{'loss': 1.3541, 'grad_norm': 0.00028524231940292767, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:34<29:17, 3.65s/it] 8%|▊ | 40/520 [02:38<29:08, 3.64s/it] {'loss': 1.3697, 'grad_norm': 0.00022368030390356548, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:38<29:08, 3.64s/it] 8%|▊ | 41/520 [02:41<29:03, 3.64s/it] {'loss': 1.3456, 'grad_norm': 0.0002376807437781709, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:41<29:03, 3.64s/it] 8%|▊ | 42/520 [02:45<28:55, 3.63s/it] {'loss': 1.3423, 'grad_norm': 0.000306305395170391, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:45<28:55, 3.63s/it] 8%|▊ | 43/520 [02:49<28:50, 3.63s/it] {'loss': 1.265, 'grad_norm': 0.0002208010693977418, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:49<28:50, 3.63s/it] 8%|▊ | 44/520 [02:52<28:48, 3.63s/it] {'loss': 1.366, 'grad_norm': 0.000249083990486524, 'learning_rate': 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10%|▉ | 50/520 [03:14<28:16, 3.61s/it] {'loss': 1.3577, 'grad_norm': 0.0002672789358993333, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:14<28:16, 3.61s/it] 10%|▉ | 51/520 [03:18<28:18, 3.62s/it] {'loss': 1.2866, 'grad_norm': 0.0002905444961781745, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:18<28:18, 3.62s/it] 10%|█ | 52/520 [03:21<28:13, 3.62s/it] {'loss': 1.427, 'grad_norm': 0.0003038322601698748, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:21<28:13, 3.62s/it] 10%|█ | 53/520 [03:25<28:06, 3.61s/it] {'loss': 1.3942, 'grad_norm': 0.0002845344381539563, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:25<28:06, 3.61s/it] 10%|█ | 54/520 [03:28<28:06, 3.62s/it] {'loss': 1.3361, 'grad_norm': 0.0002914841630971057, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:28<28:06, 3.62s/it] 11%|█ | 55/520 [03:32<28:02, 3.62s/it] {'loss': 1.2768, 'grad_norm': 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3.70s/it] 64%|██████▍ | 332/520 [20:28<11:32, 3.69s/it] {'loss': 1.2208, 'grad_norm': 0.0003928234724908623, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:28<11:32, 3.69s/it] 64%|██████▍ | 333/520 [20:32<11:26, 3.67s/it] {'loss': 1.3132, 'grad_norm': 0.0004677159396484005, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:32<11:26, 3.67s/it] 64%|██████▍ | 334/520 [20:36<11:22, 3.67s/it] {'loss': 1.2256, 'grad_norm': 0.0004658992144124646, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:36<11:22, 3.67s/it] 64%|██████▍ | 335/520 [20:39<11:15, 3.65s/it] {'loss': 1.2254, 'grad_norm': 0.0004202970773191127, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:39<11:15, 3.65s/it] 65%|██████▍ | 336/520 [20:43<11:15, 3.67s/it] {'loss': 1.1372, 'grad_norm': 0.0004845425766833228, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:43<11:15, 3.67s/it] 65%|██████▍ | 337/520 [20:47<11:10, 3.66s/it] {'loss': 1.128, 'grad_norm': 0.0004515169450831087, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:47<11:10, 3.66s/it] 65%|██████▌ | 338/520 [20:50<11:05, 3.66s/it] {'loss': 1.2321, 'grad_norm': 0.00043850197353061164, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [20:50<11:05, 3.66s/it] 65%|██████▌ | 339/520 [20:54<11:03, 3.66s/it] {'loss': 1.173, 'grad_norm': 0.0004618917203503734, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [20:54<11:03, 3.66s/it] 65%|██████▌ | 340/520 [20:58<11:02, 3.68s/it] {'loss': 1.1612, 'grad_norm': 0.00042775621636105174, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [20:58<11:02, 3.68s/it] 66%|██████▌ | 341/520 [21:01<10:58, 3.68s/it] {'loss': 1.1971, 'grad_norm': 0.0004626576580907224, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:01<10:58, 3.68s/it] 66%|██████▌ | 342/520 [21:05<10:51, 3.66s/it] {'loss': 1.2038, 'grad_norm': 0.0004928108229343697, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:05<10:51, 3.66s/it] 66%|██████▌ | 343/520 [21:08<10:46, 3.65s/it] {'loss': 1.146, 'grad_norm': 0.0003491255996187809, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:08<10:46, 3.65s/it] 66%|██████▌ | 344/520 [21:12<10:53, 3.71s/it] {'loss': 1.1522, 'grad_norm': 0.0004060245940484785, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:12<10:53, 3.71s/it] 66%|██████▋ | 345/520 [21:16<11:05, 3.81s/it] {'loss': 1.2508, 'grad_norm': 0.000457142846776214, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:16<11:05, 3.81s/it] 67%|██████▋ | 346/520 [21:20<11:04, 3.82s/it] {'loss': 1.1681, 'grad_norm': 0.00041762620165366386, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:20<11:04, 3.82s/it] 67%|██████▋ | 347/520 [21:24<11:02, 3.83s/it] {'loss': 1.1693, 'grad_norm': 0.00040156283819091983, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:24<11:02, 3.83s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:28<10:59, 3.83s/it] {'loss': 1.1246, 'grad_norm': 0.0005148401163655356, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:28<10:59, 3.83s/it] 67%|██████▋ | 349/520 [21:32<10:56, 3.84s/it] {'loss': 1.1582, 'grad_norm': 0.00045138952714503065, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:32<10:56, 3.84s/it] 67%|██████▋ | 350/520 [21:36<10:52, 3.84s/it] {'loss': 1.2063, 'grad_norm': 0.000456044708510131, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:36<10:52, 3.84s/it] 68%|██████▊ | 351/520 [21:39<10:52, 3.86s/it] {'loss': 1.1187, 'grad_norm': 0.0004213356303172076, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:39<10:52, 3.86s/it] 68%|██████▊ | 352/520 [21:43<10:47, 3.85s/it] {'loss': 1.2288, 'grad_norm': 0.00040935730484032844, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:43<10:47, 3.85s/it] 68%|██████▊ | 353/520 [21:47<10:46, 3.87s/it] {'loss': 1.1475, 'grad_norm': 0.00036238963665006217, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:47<10:46, 3.87s/it] 68%|██████▊ | 354/520 [21:51<10:40, 3.86s/it] {'loss': 1.2348, 'grad_norm': 0.0003922231939464365, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [21:51<10:40, 3.86s/it] 68%|██████▊ | 355/520 [21:55<10:36, 3.86s/it] {'loss': 1.1824, 'grad_norm': 0.000446443013930549, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [21:55<10:36, 3.86s/it] 68%|██████▊ | 356/520 [21:59<10:32, 3.85s/it] {'loss': 1.1822, 'grad_norm': 0.0004515600014569986, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [21:59<10:32, 3.85s/it] 69%|██████▊ | 357/520 [22:03<10:27, 3.85s/it] {'loss': 1.2162, 'grad_norm': 0.00041863355744206746, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:03<10:27, 3.85s/it] 69%|██████▉ | 358/520 [22:06<10:24, 3.86s/it] {'loss': 1.147, 'grad_norm': 0.0004442736077863643, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:06<10:24, 3.86s/it] 69%|██████▉ | 359/520 [22:10<10:20, 3.85s/it] {'loss': 1.1798, 'grad_norm': 0.0004325718608959147, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:10<10:20, 3.85s/it] 69%|██████▉ | 360/520 [22:14<10:16, 3.86s/it] {'loss': 1.1828, 'grad_norm': 0.000419184499197693, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:14<10:16, 3.86s/it] 69%|██████▉ | 361/520 [22:18<10:12, 3.85s/it] {'loss': 1.2053, 'grad_norm': 0.0003809717498693295, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:18<10:12, 3.85s/it] 70%|██████▉ | 362/520 [22:22<10:08, 3.85s/it] {'loss': 1.1891, 'grad_norm': 0.0004826023686597, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:22<10:08, 3.85s/it] 70%|██████▉ | 363/520 [22:26<10:05, 3.85s/it] {'loss': 1.2215, 'grad_norm': 0.0004353242227077305, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:26<10:05, 3.85s/it] 70%|███████ | 364/520 [22:30<10:11, 3.92s/it] {'loss': 1.2145, 'grad_norm': 0.0004266601492788858, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:30<10:11, 3.92s/it] 70%|███████ | 365/520 [22:34<10:13, 3.96s/it] {'loss': 1.2716, 'grad_norm': 0.00044495312362542917, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:34<10:13, 3.96s/it] 70%|███████ | 366/520 [22:38<10:15, 4.00s/it] {'loss': 1.244, 'grad_norm': 0.0004247362173077013, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:38<10:15, 4.00s/it] 71%|███████ | 367/520 [22:42<10:15, 4.03s/it] {'loss': 1.2373, 'grad_norm': 0.00044815657923239237, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:42<10:15, 4.03s/it] 71%|███████ | 368/520 [22:46<10:14, 4.04s/it] {'loss': 1.0893, 'grad_norm': 0.0004448802560375408, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:46<10:14, 4.04s/it] 71%|███████ | 369/520 [22:50<10:10, 4.05s/it] {'loss': 1.1816, 'grad_norm': 0.0003895177025844714, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:50<10:10, 4.05s/it] 71%|███████ | 370/520 [22:54<10:07, 4.05s/it] {'loss': 1.1527, 'grad_norm': 0.00041684241455505674, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:54<10:07, 4.05s/it] 71%|███████▏ | 371/520 [22:58<10:03, 4.05s/it] {'loss': 1.1378, 'grad_norm': 0.00046865322200861307, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [22:58<10:03, 4.05s/it] 72%|███████▏ | 372/520 [23:02<10:01, 4.07s/it] {'loss': 1.2444, 'grad_norm': 0.00037568304999687727, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:02<10:01, 4.07s/it] 72%|███████▏ | 373/520 [23:07<09:58, 4.07s/it] {'loss': 1.135, 'grad_norm': 0.0004572921152839423, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:07<09:58, 4.07s/it] 72%|███████▏ | 374/520 [23:11<09:54, 4.07s/it] {'loss': 1.2355, 'grad_norm': 0.000464395242033656, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:11<09:54, 4.07s/it] 72%|███████▏ | 375/520 [23:15<09:50, 4.07s/it] {'loss': 1.1523, 'grad_norm': 0.00044833276943497593, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:15<09:50, 4.07s/it] 72%|███████▏ | 376/520 [23:18<09:37, 4.01s/it] {'loss': 1.2585, 'grad_norm': 0.0004255935525068799, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:19<09:37, 4.01s/it] 72%|███████▎ | 377/520 [23:22<09:26, 3.96s/it] {'loss': 1.188, 'grad_norm': 0.00046805508290985123, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:22<09:26, 3.96s/it] 73%|███████▎ | 378/520 [23:26<09:26, 3.99s/it] {'loss': 1.252, 'grad_norm': 0.0004119899941618831, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:26<09:26, 3.99s/it] 73%|███████▎ | 379/520 [23:31<09:28, 4.04s/it] {'loss': 1.213, 'grad_norm': 0.0004051496880905435, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:31<09:28, 4.04s/it] 73%|███████▎ | 380/520 [23:35<09:27, 4.05s/it] {'loss': 1.2256, 'grad_norm': 0.00043011758238266357, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:35<09:27, 4.05s/it] 73%|███████▎ | 381/520 [23:39<09:18, 4.02s/it] {'loss': 1.229, 'grad_norm': 0.0004212068515693464, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:39<09:18, 4.02s/it] 73%|███████▎ | 382/520 [23:42<09:09, 3.98s/it] {'loss': 1.1999, 'grad_norm': 0.00039121156063028333, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:42<09:09, 3.98s/it] 74%|███████▎ | 383/520 [23:46<08:59, 3.94s/it] {'loss': 1.0723, 'grad_norm': 0.000481800865414428, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:46<08:59, 3.94s/it] 74%|███████▍ | 384/520 [23:50<08:50, 3.90s/it] {'loss': 1.2115, 'grad_norm': 0.0003623519313055743, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:50<08:50, 3.90s/it] 74%|███████▍ | 385/520 [23:54<08:44, 3.88s/it] {'loss': 1.2192, 'grad_norm': 0.0004116212775101915, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:54<08:44, 3.88s/it] 74%|███████▍ | 386/520 [23:58<08:39, 3.87s/it] {'loss': 1.1648, 'grad_norm': 0.00037525724109621535, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:58<08:39, 3.87s/it] 74%|███████▍ | 387/520 [24:02<08:33, 3.86s/it] {'loss': 1.2424, 'grad_norm': 0.0004136259041111672, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:02<08:33, 3.86s/it] 75%|███████▍ | 388/520 [24:05<08:27, 3.84s/it] {'loss': 1.1285, 'grad_norm': 0.0004240293865128445, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:05<08:27, 3.84s/it] 75%|███████▍ | 389/520 [24:09<08:22, 3.83s/it] {'loss': 1.1764, 'grad_norm': 0.000557314076562611, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:09<08:22, 3.83s/it] 75%|███████▌ | 390/520 [24:13<08:20, 3.85s/it] {'loss': 1.2439, 'grad_norm': 0.00041368712501865736, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:13<08:20, 3.85s/it] 75%|███████▌ | 391/520 [24:17<08:16, 3.85s/it] {'loss': 1.2977, 'grad_norm': 0.00043728430224255967, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:17<08:16, 3.85s/it] 75%|███████▌ | 392/520 [24:21<08:11, 3.84s/it] {'loss': 1.1282, 'grad_norm': 0.00042656489467408367, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:21<08:11, 3.84s/it] 76%|███████▌ | 393/520 [24:25<08:06, 3.83s/it] {'loss': 1.1097, 'grad_norm': 0.00036690915084133216, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:25<08:06, 3.83s/it] 76%|███████▌ | 394/520 [24:28<08:02, 3.83s/it] {'loss': 1.2023, 'grad_norm': 0.00045858876965437057, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:28<08:02, 3.83s/it] 76%|███████▌ | 395/520 [24:32<07:56, 3.82s/it] {'loss': 1.1686, 'grad_norm': 0.0004663817099857774, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:32<07:56, 3.82s/it] 76%|███████▌ | 396/520 [24:36<07:44, 3.75s/it] {'loss': 1.2379, 'grad_norm': 0.00045767619508909203, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:36<07:44, 3.75s/it] 76%|███████▋ | 397/520 [24:39<07:36, 3.71s/it] {'loss': 1.2143, 'grad_norm': 0.00042825453232434946, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:39<07:36, 3.71s/it] 77%|███████▋ | 398/520 [24:43<07:30, 3.69s/it] {'loss': 1.2045, 'grad_norm': 0.0004611857172746619, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:43<07:30, 3.69s/it] 77%|███████▋ | 399/520 [24:47<07:25, 3.68s/it] {'loss': 1.1366, 'grad_norm': 0.0004090797766159617, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:47<07:25, 3.68s/it] 77%|███████▋ | 400/520 [24:50<07:20, 3.67s/it] {'loss': 1.1716, 'grad_norm': 0.0003907624978403341, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:50<07:20, 3.67s/it] 77%|███████▋ | 401/520 [24:54<07:16, 3.67s/it] {'loss': 1.0519, 'grad_norm': 0.00047577419948306737, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:54<07:16, 3.67s/it] 77%|███████▋ | 402/520 [24:58<07:11, 3.66s/it] {'loss': 1.1824, 'grad_norm': 0.000452431960846218, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:58<07:11, 3.66s/it] 78%|███████▊ | 403/520 [25:01<07:05, 3.64s/it] {'loss': 1.2024, 'grad_norm': 0.00047159039281959204, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:01<07:05, 3.64s/it] 78%|███████▊ | 404/520 [25:05<07:00, 3.63s/it] {'loss': 1.1185, 'grad_norm': 0.0005127010166799979, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:05<07:00, 3.63s/it] 78%|███████▊ | 405/520 [25:09<06:57, 3.63s/it] {'loss': 1.1541, 'grad_norm': 0.0004072921971635818, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:09<06:57, 3.63s/it] 78%|███████▊ | 406/520 [25:12<06:54, 3.63s/it] {'loss': 1.0821, 'grad_norm': 0.0005048388467898191, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:12<06:54, 3.63s/it] 78%|███████▊ | 407/520 [25:16<06:50, 3.64s/it] {'loss': 1.276, 'grad_norm': 0.00043992879468844625, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:16<06:50, 3.64s/it] 78%|███████▊ | 408/520 [25:19<06:47, 3.64s/it] {'loss': 1.1964, 'grad_norm': 0.0004905798846803423, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:19<06:47, 3.64s/it] 79%|███████▊ | 409/520 [25:23<06:43, 3.63s/it] {'loss': 1.313, 'grad_norm': 0.0004622953437119971, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:23<06:43, 3.63s/it] 79%|███████▉ | 410/520 [25:27<06:38, 3.63s/it] {'loss': 1.0527, 'grad_norm': 0.00045103438796734944, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:27<06:38, 3.63s/it] 79%|███████▉ | 411/520 [25:30<06:35, 3.63s/it] {'loss': 1.291, 'grad_norm': 0.00046579070022501734, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:30<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:34<06:31, 3.63s/it] {'loss': 1.205, 'grad_norm': 0.00042857737763360264, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:34<06:31, 3.63s/it] 79%|███████▉ | 413/520 [25:38<06:28, 3.63s/it] {'loss': 1.1749, 'grad_norm': 0.00040866422826251776, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:38<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:41<06:31, 3.69s/it] {'loss': 0.9819, 'grad_norm': 0.00036014737693796555, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:41<06:31, 3.69s/it] 80%|███████▉ | 415/520 [25:45<06:30, 3.72s/it] {'loss': 1.1848, 'grad_norm': 0.0004168324320197827, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:45<06:30, 3.72s/it] 80%|████████ | 416/520 [25:49<06:26, 3.72s/it] {'loss': 1.0879, 'grad_norm': 0.0004841233495177455, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:49<06:26, 3.72s/it] 80%|████████ | 417/520 [25:53<06:20, 3.70s/it] {'loss': 1.2472, 'grad_norm': 0.00043057653222099005, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:53<06:20, 3.70s/it] 80%|████████ | 418/520 [25:56<06:18, 3.71s/it] {'loss': 1.2451, 'grad_norm': 0.0004085470948400891, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:56<06:18, 3.71s/it] 81%|████████ | 419/520 [26:00<06:16, 3.73s/it] {'loss': 1.2422, 'grad_norm': 0.00047634918346385065, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:00<06:16, 3.73s/it] 81%|████████ | 420/520 [26:04<06:15, 3.75s/it] {'loss': 1.1287, 'grad_norm': 0.0004534868965282576, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:04<06:15, 3.75s/it] 81%|████████ | 421/520 [26:08<06:13, 3.77s/it] {'loss': 1.0664, 'grad_norm': 0.0004643481871541693, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:08<06:13, 3.77s/it] 81%|████████ | 422/520 [26:12<06:10, 3.78s/it] {'loss': 1.1902, 'grad_norm': 0.0004618892878272523, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:12<06:10, 3.78s/it] 81%|████████▏ | 423/520 [26:15<06:07, 3.79s/it] {'loss': 1.1566, 'grad_norm': 0.00047103685693003993, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:15<06:07, 3.79s/it] 82%|████████▏ | 424/520 [26:19<06:01, 3.76s/it] {'loss': 1.2507, 'grad_norm': 0.00038838012116856885, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:19<06:01, 3.76s/it] 82%|████████▏ | 425/520 [26:23<05:54, 3.73s/it] {'loss': 1.1674, 'grad_norm': 0.0004288268730990717, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:23<05:54, 3.73s/it] 82%|████████▏ | 426/520 [26:26<05:47, 3.69s/it] {'loss': 1.2123, 'grad_norm': 0.0005690632252223821, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:26<05:47, 3.69s/it] 82%|████████▏ | 427/520 [26:30<05:41, 3.67s/it] {'loss': 1.1009, 'grad_norm': 0.0004205981940576579, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:30<05:41, 3.67s/it] 82%|████████▏ | 428/520 [26:34<05:38, 3.68s/it] {'loss': 1.1005, 'grad_norm': 0.0004807306935930221, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:34<05:38, 3.68s/it] 82%|████████▎ | 429/520 [26:37<05:36, 3.69s/it] {'loss': 1.202, 'grad_norm': 0.0004477710578031314, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:37<05:36, 3.69s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:41<05:30, 3.67s/it] {'loss': 1.202, 'grad_norm': 0.00042432859379428053, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:41<05:30, 3.67s/it] 83%|████████▎ | 431/520 [26:45<05:25, 3.66s/it] {'loss': 1.1421, 'grad_norm': 0.00041365147046991114, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:45<05:25, 3.66s/it] 83%|████████▎ | 432/520 [26:48<05:23, 3.67s/it] {'loss': 1.1039, 'grad_norm': 0.0004635166125656687, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:48<05:23, 3.67s/it] 83%|████████▎ | 433/520 [26:52<05:20, 3.68s/it] {'loss': 1.2389, 'grad_norm': 0.0004392241812220847, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:52<05:20, 3.68s/it] 83%|████████▎ | 434/520 [26:56<05:16, 3.68s/it] {'loss': 0.9978, 'grad_norm': 0.00045905555586611354, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:56<05:16, 3.68s/it] 84%|████████▎ | 435/520 [26:59<05:12, 3.67s/it] {'loss': 1.2723, 'grad_norm': 0.0004696078292845001, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:59<05:12, 3.67s/it] 84%|████████▍ | 436/520 [27:03<05:08, 3.67s/it] {'loss': 1.0833, 'grad_norm': 0.0004663110651469541, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:03<05:08, 3.67s/it] 84%|████████▍ | 437/520 [27:07<05:03, 3.65s/it] {'loss': 1.2925, 'grad_norm': 0.00044841399201309655, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:07<05:03, 3.65s/it] 84%|████████▍ | 438/520 [27:10<05:00, 3.66s/it] {'loss': 1.1111, 'grad_norm': 0.0004479676222606495, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:10<05:00, 3.66s/it] 84%|████████▍ | 439/520 [27:14<04:55, 3.65s/it] {'loss': 1.1278, 'grad_norm': 0.0003526795610997326, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:14<04:55, 3.65s/it] 85%|████████▍ | 440/520 [27:18<04:52, 3.65s/it] {'loss': 1.1509, 'grad_norm': 0.00047114380497523153, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:18<04:52, 3.65s/it] 85%|████████▍ | 441/520 [27:21<04:48, 3.65s/it] {'loss': 1.1385, 'grad_norm': 0.0004334748612268936, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:21<04:48, 3.65s/it] 85%|████████▌ | 442/520 [27:25<04:43, 3.64s/it] {'loss': 1.2134, 'grad_norm': 0.0004896062827727271, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:25<04:43, 3.64s/it] 85%|████████▌ | 443/520 [27:28<04:39, 3.63s/it] {'loss': 1.2175, 'grad_norm': 0.00042793577430034476, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:28<04:39, 3.63s/it] 85%|████████▌ | 444/520 [27:32<04:36, 3.64s/it] {'loss': 1.1863, 'grad_norm': 0.0003931505008993219, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:32<04:36, 3.64s/it] 86%|████████▌ | 445/520 [27:36<04:32, 3.64s/it] {'loss': 1.1118, 'grad_norm': 0.0004174197949396467, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:36<04:32, 3.64s/it] 86%|████████▌ | 446/520 [27:39<04:29, 3.64s/it] {'loss': 1.2171, 'grad_norm': 0.00038212366667667844, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:39<04:29, 3.64s/it] 86%|████████▌ | 447/520 [27:43<04:25, 3.64s/it] {'loss': 1.1817, 'grad_norm': 0.00043208953393432343, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:43<04:25, 3.64s/it] 86%|████████▌ | 448/520 [27:47<04:21, 3.64s/it] {'loss': 1.1831, 'grad_norm': 0.0004987650009550432, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:47<04:21, 3.64s/it] 86%|████████▋ | 449/520 [27:50<04:17, 3.63s/it] {'loss': 1.1784, 'grad_norm': 0.00042061777593469836, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:50<04:17, 3.63s/it] 87%|████████▋ | 450/520 [27:54<04:14, 3.64s/it] {'loss': 1.2076, 'grad_norm': 0.0004544403069659482, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:54<04:14, 3.64s/it] 87%|████████▋ | 451/520 [27:58<04:11, 3.64s/it] {'loss': 1.2096, 'grad_norm': 0.00045353843003721024, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:58<04:11, 3.64s/it] 87%|████████▋ | 452/520 [28:01<04:07, 3.64s/it] {'loss': 1.2261, 'grad_norm': 0.0004020912360183626, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:01<04:07, 3.64s/it] 87%|████████▋ | 453/520 [28:05<04:04, 3.64s/it] {'loss': 1.2027, 'grad_norm': 0.0004114157509681599, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:05<04:04, 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'epoch': 1.0} + 100%|██████████| 520/520 [32:10<00:00, 3.87s/it] 100%|██████████| 520/520 [32:10<00:00, 3.71s/it] +[2025-10-18 03:04:49,577] [INFO] [launch.py:348:main] Process 876689 exits successfully. +[2025-10-18 03:04:49,577] [INFO] [launch.py:348:main] Process 876693 exits successfully. +[2025-10-18 03:04:50,579] [INFO] [launch.py:348:main] Process 876694 exits successfully. +[2025-10-18 03:04:50,579] [INFO] [launch.py:348:main] Process 876690 exits successfully. +[2025-10-18 03:04:50,580] [INFO] [launch.py:348:main] Process 876691 exits successfully. +[2025-10-18 03:04:50,580] [INFO] [launch.py:348:main] Process 876695 exits successfully. +[2025-10-18 03:04:50,581] [INFO] [launch.py:348:main] Process 876692 exits successfully. +[2025-10-18 03:04:54,586] [INFO] [launch.py:348:main] Process 876688 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.7_2e-1_connector-9.0_1.7_2e-1_ablation_20251018_023103.log +Timestamp: 2025-10-18 03:04:57 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation_20251018_030457.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation_20251018_030457.log new file mode 100644 index 0000000000000000000000000000000000000000..d2280504fee0ffeba8a823a1bb5aff24211d08b6 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation_20251018_030457.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation_20251018_030457.log +Timestamp: 2025-10-18 03:04:57 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:04:59,722] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:02,422] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 03:05:02,423] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 1.9 --temperature_mlp_text 1.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 1.9 --temperature_mlp_vision 1.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 1.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:05:04,990] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:06,085] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 03:05:06,085] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 03:05:06,085] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 03:05:06,085] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 03:05:06,085] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 03:05:06,085] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 03:05:06,085] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 03:05:06,088] [INFO] [launch.py:253:main] process 898636 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,089] [INFO] [launch.py:253:main] process 898637 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,091] [INFO] [launch.py:253:main] process 898638 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,093] [INFO] [launch.py:253:main] process 898639 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,095] [INFO] [launch.py:253:main] process 898640 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,097] [INFO] [launch.py:253:main] process 898641 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,099] [INFO] [launch.py:253:main] process 898642 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:05:06,101] [INFO] [launch.py:253:main] process 898643 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '1.9', '--temperature_mlp_text', '1.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '1.9', '--temperature_mlp_vision', '1.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '1.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:05:12,783] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:12,986] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,030] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,030] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,031] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,043] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,047] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,047] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:05:13,221] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,221] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 03:05:13,397] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,448] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,451] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,461] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,472] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,479] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:05:13,496] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 1.9, 'temperature_mlp': 1.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 1.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 1.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 1.9, + "temperature_mlp": 1.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:898641:898641 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:898638:898638 [2] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:898639:898639 [3] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:898637:898637 [1] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:898643:900288 [7] 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START +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO ncclCommInitRank comm 0x555d620a76c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x6e5811612f2f69e6 - Init START +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO ncclCommInitRank comm 0x5579cc479950 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x6e5811612f2f69e6 - Init START +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO ncclCommInitRank comm 0x55c57a2c3230 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x6e5811612f2f69e6 - Init START +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO ncclCommInitRank comm 0x55b794b9c8c0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x6e5811612f2f69e6 - Init START +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO ncclCommInitRank comm 0x55a8206a9120 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x6e5811612f2f69e6 - Init START +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Setting affinity for GPU 6 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO NVLS multicast support is not available on dev 6 +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO NVLS multicast support is not available on dev 1 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Setting affinity for GPU 4 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO NVLS multicast support is not available on dev 4 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO NVLS multicast support is not available on dev 0 +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Setting affinity for GPU 5 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Setting affinity for GPU 2 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO NVLS multicast support is not available on dev 2 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Setting affinity for GPU 7 to ffffff00,0000ffff,ff000000 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO NVLS multicast support is not available on dev 7 +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO NVLS multicast support is not available on dev 5 +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Setting affinity for GPU 3 to ff,ffff0000,00ffffff +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO NVLS multicast support is not available on dev 3 +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO comm 0x5596b7b7eba0 rank 3 nRanks 8 nNodes 1 localRanks 8 localRank 3 MNNVL 0 +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO comm 0x55b794b9c8c0 rank 2 nRanks 8 nNodes 1 localRanks 8 localRank 2 MNNVL 0 +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO comm 0x55a457508680 rank 1 nRanks 8 nNodes 1 localRanks 8 localRank 1 MNNVL 0 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO comm 0x55a8206a9120 rank 7 nRanks 8 nNodes 1 localRanks 8 localRank 7 MNNVL 0 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO comm 0x5579cc479950 rank 0 nRanks 8 nNodes 1 localRanks 8 localRank 0 MNNVL 0 +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO comm 0x55c57a2c3230 rank 4 nRanks 8 nNodes 1 localRanks 8 localRank 4 MNNVL 0 +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO comm 0x563497fda670 rank 6 nRanks 8 nNodes 1 localRanks 8 localRank 6 MNNVL 0 +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO comm 0x555d620a76c0 rank 5 nRanks 8 nNodes 1 localRanks 8 localRank 5 MNNVL 0 +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 00/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898642:900287 [6] NCCL INFO ncclCommInitRank comm 0x563497fda670 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x6e5811612f2f69e6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898641:900285 [5] NCCL INFO ncclCommInitRank comm 0x555d620a76c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x6e5811612f2f69e6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898639:900289 [3] NCCL INFO ncclCommInitRank comm 0x5596b7b7eba0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x6e5811612f2f69e6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898636:900264 [0] NCCL INFO ncclCommInitRank comm 0x5579cc479950 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x6e5811612f2f69e6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898637:900296 [1] NCCL INFO ncclCommInitRank comm 0x55a457508680 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x6e5811612f2f69e6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898640:900284 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:898643:900288 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:898638:900286 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so 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/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 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'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 03:06:02,073] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 03:06:21,192 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 03:06:21,197 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:001->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898636:905307 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898640:905312 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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per peer +ywang29-vrdb-test1-worker-0:898641:905308 [5] NCCL INFO ncclCommInitRank comm 0x7f50e006b330 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xbdf68ca4a77c46b1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898643:905310 [7] NCCL INFO ncclCommInitRank comm 0x7f025c06af30 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xbdf68ca4a77c46b1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898639:905314 [3] NCCL INFO ncclCommInitRank comm 0x7f8ff806b2f0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xbdf68ca4a77c46b1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898637:905309 [1] NCCL INFO ncclCommInitRank comm 0x7f4e1006b000 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xbdf68ca4a77c46b1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898638:905313 [2] NCCL INFO ncclCommInitRank comm 0x7f8fe406a570 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xbdf68ca4a77c46b1 - Init COMPLETE +ywang29-vrdb-test1-worker-0:898642:905311 [6] NCCL INFO 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'epoch': 0.02} + 2%|▏ | 9/520 [00:46<36:14, 4.26s/it] 2%|▏ | 10/520 [00:49<34:35, 4.07s/it] {'loss': 1.5164, 'grad_norm': 0.0006656285946085688, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:49<34:35, 4.07s/it] 2%|▏ | 11/520 [00:53<33:47, 3.98s/it] {'loss': 1.5181, 'grad_norm': 0.00045877290284496836, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:53<33:47, 3.98s/it] 2%|▏ | 12/520 [00:57<32:51, 3.88s/it] {'loss': 1.3869, 'grad_norm': 0.00035000914554690995, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:57<32:51, 3.88s/it][2025-10-18 03:07:27,045] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:01<33:55, 4.02s/it] {'loss': 1.4816, 'grad_norm': 0.00036233081784056535, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:01<33:55, 4.02s/it] 3%|▎ | 14/520 [01:05<32:54, 3.90s/it] {'loss': 1.5019, 'grad_norm': 0.0003135361835082712, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:05<32:54, 3.90s/it] 3%|▎ | 15/520 [01:08<32:09, 3.82s/it] {'loss': 1.4055, 'grad_norm': 0.00023946798873781893, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:08<32:09, 3.82s/it] 3%|▎ | 16/520 [01:12<31:34, 3.76s/it] {'loss': 1.3775, 'grad_norm': 0.0002981310637581463, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:12<31:34, 3.76s/it] 3%|▎ | 17/520 [01:16<31:07, 3.71s/it] {'loss': 1.5139, 'grad_norm': 0.0002987628430532582, 'learning_rate': 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4%|▍ | 23/520 [01:37<30:10, 3.64s/it] {'loss': 1.4133, 'grad_norm': 0.00026675129145845586, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:37<30:10, 3.64s/it] 5%|▍ | 24/520 [01:41<30:00, 3.63s/it] {'loss': 1.3316, 'grad_norm': 0.00029392131490741, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:41<30:00, 3.63s/it] 5%|▍ | 25/520 [01:45<29:54, 3.63s/it] {'loss': 1.4231, 'grad_norm': 0.0003290633090103223, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:45<29:54, 3.63s/it] 5%|▌ | 26/520 [01:48<29:48, 3.62s/it] {'loss': 1.3571, 'grad_norm': 0.0002899915369956758, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<29:48, 3.62s/it] 5%|▌ | 27/520 [01:52<29:40, 3.61s/it] {'loss': 1.2859, 'grad_norm': 0.0002599238740225359, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:52<29:40, 3.61s/it] 5%|▌ | 28/520 [01:55<29:36, 3.61s/it] {'loss': 1.3205, 'grad_norm': 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0.00044033664058376013, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:30<13:25, 3.87s/it] 60%|██████ | 313/520 [19:34<13:20, 3.87s/it] {'loss': 1.1094, 'grad_norm': 0.0004064566703702103, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:34<13:20, 3.87s/it] 60%|██████ | 314/520 [19:38<13:46, 4.01s/it] {'loss': 1.1551, 'grad_norm': 0.0004119484165126136, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:38<13:46, 4.01s/it] 61%|██████ | 315/520 [19:42<13:32, 3.96s/it] {'loss': 1.1826, 'grad_norm': 0.000463183038570194, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:42<13:32, 3.96s/it] 61%|██████ | 316/520 [19:46<13:50, 4.07s/it] {'loss': 1.1453, 'grad_norm': 0.0004565773794610915, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:46<13:50, 4.07s/it] 61%|██████ | 317/520 [19:50<13:33, 4.01s/it] {'loss': 1.1397, 'grad_norm': 0.0003912932029492792, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:50<13:33, 4.01s/it] 61%|██████ | 318/520 [19:54<13:19, 3.96s/it] {'loss': 1.2519, 'grad_norm': 0.0004464154633891735, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:54<13:19, 3.96s/it] 61%|██████▏ | 319/520 [19:58<13:39, 4.08s/it] {'loss': 1.1374, 'grad_norm': 0.00038523763169938933, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:58<13:39, 4.08s/it] 62%|██████▏ | 320/520 [20:02<13:22, 4.01s/it] {'loss': 1.0741, 'grad_norm': 0.0004314470697536279, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [20:02<13:22, 4.01s/it] 62%|██████▏ | 321/520 [20:06<13:09, 3.97s/it] {'loss': 1.274, 'grad_norm': 0.0004291216912524537, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:06<13:09, 3.97s/it] 62%|██████▏ | 322/520 [20:10<12:55, 3.92s/it] {'loss': 1.0846, 'grad_norm': 0.00040820885865053324, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:10<12:55, 3.92s/it] 62%|██████▏ | 323/520 [20:14<12:43, 3.88s/it] {'loss': 1.1608, 'grad_norm': 0.0004200300929292035, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:14<12:43, 3.88s/it] 62%|██████▏ | 324/520 [20:18<12:48, 3.92s/it] {'loss': 1.2207, 'grad_norm': 0.0004342673652187236, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:18<12:48, 3.92s/it] 62%|██████▎ | 325/520 [20:22<12:53, 3.97s/it] {'loss': 1.2117, 'grad_norm': 0.00045012700978686835, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:22<12:53, 3.97s/it] 63%|██████▎ | 326/520 [20:26<12:42, 3.93s/it] {'loss': 1.2163, 'grad_norm': 0.00045782730138253173, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:26<12:42, 3.93s/it] 63%|██████▎ | 327/520 [20:29<12:34, 3.91s/it] {'loss': 1.1876, 'grad_norm': 0.0004372779798546312, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:29<12:34, 3.91s/it] 63%|██████▎ | 328/520 [20:33<12:26, 3.89s/it] {'loss': 1.2527, 'grad_norm': 0.00044107354325062504, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:33<12:26, 3.89s/it] 63%|██████▎ | 329/520 [20:37<12:20, 3.88s/it] {'loss': 1.1379, 'grad_norm': 0.0003817036845453986, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:37<12:20, 3.88s/it] 63%|██████▎ | 330/520 [20:41<12:11, 3.85s/it] {'loss': 1.2161, 'grad_norm': 0.0004099821913224154, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:41<12:11, 3.85s/it] 64%|██████▎ | 331/520 [20:45<11:54, 3.78s/it] {'loss': 1.1712, 'grad_norm': 0.0004496299006871463, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:45<11:54, 3.78s/it] 64%|██████▍ | 332/520 [20:48<11:45, 3.75s/it] {'loss': 1.2159, 'grad_norm': 0.00038786397570232835, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:48<11:45, 3.75s/it] 64%|██████▍ | 333/520 [20:52<11:35, 3.72s/it] {'loss': 1.3063, 'grad_norm': 0.0004533523471800065, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:52<11:35, 3.72s/it] 64%|██████▍ | 334/520 [20:55<11:27, 3.69s/it] {'loss': 1.2183, 'grad_norm': 0.00045272106494080214, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:55<11:27, 3.69s/it] 64%|██████▍ | 335/520 [20:59<11:20, 3.68s/it] {'loss': 1.22, 'grad_norm': 0.00042134472984266545, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:59<11:20, 3.68s/it] 65%|██████▍ | 336/520 [21:03<11:14, 3.66s/it] {'loss': 1.1309, 'grad_norm': 0.0004759453347629098, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [21:03<11:14, 3.66s/it] 65%|██████▍ | 337/520 [21:06<11:09, 3.66s/it] {'loss': 1.1229, 'grad_norm': 0.00044469246648684254, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:06<11:09, 3.66s/it] 65%|██████▌ | 338/520 [21:10<11:04, 3.65s/it] {'loss': 1.2271, 'grad_norm': 0.00043187500534865915, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:10<11:04, 3.65s/it] 65%|██████▌ | 339/520 [21:14<11:00, 3.65s/it] {'loss': 1.1667, 'grad_norm': 0.00046262168569615055, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:14<11:00, 3.65s/it] 65%|██████▌ | 340/520 [21:17<10:57, 3.65s/it] {'loss': 1.1547, 'grad_norm': 0.00042783509817572504, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:17<10:57, 3.65s/it] 66%|██████▌ | 341/520 [21:21<10:50, 3.64s/it] {'loss': 1.1892, 'grad_norm': 0.0004629890106337141, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:21<10:50, 3.64s/it] 66%|██████▌ | 342/520 [21:25<10:46, 3.63s/it] {'loss': 1.1977, 'grad_norm': 0.00048581584835253407, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:25<10:46, 3.63s/it] 66%|██████▌ | 343/520 [21:28<10:43, 3.63s/it] {'loss': 1.1411, 'grad_norm': 0.00036108101294603185, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:28<10:43, 3.63s/it] 66%|██████▌ | 344/520 [21:32<10:39, 3.63s/it] {'loss': 1.1458, 'grad_norm': 0.00040292553942892476, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:32<10:39, 3.63s/it] 66%|██████▋ | 345/520 [21:35<10:35, 3.63s/it] {'loss': 1.2433, 'grad_norm': 0.0004516025058336014, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:35<10:35, 3.63s/it] 67%|██████▋ | 346/520 [21:39<10:32, 3.63s/it] {'loss': 1.1636, 'grad_norm': 0.0004241509613759259, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:39<10:32, 3.63s/it] 67%|██████▋ | 347/520 [21:43<10:31, 3.65s/it] {'loss': 1.1624, 'grad_norm': 0.000394077617784776, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:43<10:31, 3.65s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:47<10:37, 3.71s/it] {'loss': 1.119, 'grad_norm': 0.0005065688315767042, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:47<10:37, 3.71s/it] 67%|██████▋ | 349/520 [21:50<10:39, 3.74s/it] {'loss': 1.1525, 'grad_norm': 0.00043482082914809224, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:50<10:39, 3.74s/it] 67%|██████▋ | 350/520 [21:54<10:38, 3.76s/it] {'loss': 1.1982, 'grad_norm': 0.0004841198829399025, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:54<10:38, 3.76s/it] 68%|██████▊ | 351/520 [21:58<10:40, 3.79s/it] {'loss': 1.1116, 'grad_norm': 0.0004169809437825915, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:58<10:40, 3.79s/it] 68%|██████▊ | 352/520 [22:02<10:34, 3.78s/it] {'loss': 1.2223, 'grad_norm': 0.0004054448123710922, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:02<10:34, 3.78s/it] 68%|██████▊ | 353/520 [22:06<10:25, 3.75s/it] {'loss': 1.1413, 'grad_norm': 0.000380329589592018, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:06<10:25, 3.75s/it] 68%|██████▊ | 354/520 [22:09<10:24, 3.76s/it] {'loss': 1.2278, 'grad_norm': 0.00039716761247478516, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:09<10:24, 3.76s/it] 68%|██████▊ | 355/520 [22:13<10:23, 3.78s/it] {'loss': 1.1756, 'grad_norm': 0.0004437824752530679, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:13<10:23, 3.78s/it] 68%|██████▊ | 356/520 [22:17<10:22, 3.80s/it] {'loss': 1.1737, 'grad_norm': 0.00043971377868980014, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:17<10:22, 3.80s/it] 69%|██████▊ | 357/520 [22:21<10:20, 3.81s/it] {'loss': 1.2109, 'grad_norm': 0.0004091974336832095, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:21<10:20, 3.81s/it] 69%|██████▉ | 358/520 [22:25<10:17, 3.81s/it] {'loss': 1.1376, 'grad_norm': 0.0004310880978718746, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:25<10:17, 3.81s/it] 69%|██████▉ | 359/520 [22:28<10:14, 3.82s/it] {'loss': 1.1735, 'grad_norm': 0.00042332013595228267, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:28<10:14, 3.82s/it] 69%|██████▉ | 360/520 [22:32<10:11, 3.82s/it] {'loss': 1.1776, 'grad_norm': 0.0004243188813729153, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:32<10:11, 3.82s/it] 69%|██████▉ | 361/520 [22:36<10:07, 3.82s/it] {'loss': 1.1986, 'grad_norm': 0.000372865621706552, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:36<10:07, 3.82s/it] 70%|██████▉ | 362/520 [22:40<10:04, 3.82s/it] {'loss': 1.1824, 'grad_norm': 0.00047303598147072415, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:40<10:04, 3.82s/it] 70%|██████▉ | 363/520 [22:44<10:01, 3.83s/it] {'loss': 1.2139, 'grad_norm': 0.0004236447771120636, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:44<10:01, 3.83s/it] 70%|███████ | 364/520 [22:48<09:57, 3.83s/it] {'loss': 1.2082, 'grad_norm': 0.0004195920543872719, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:48<09:57, 3.83s/it] 70%|███████ | 365/520 [22:51<09:52, 3.83s/it] {'loss': 1.2644, 'grad_norm': 0.00043732455830938077, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:51<09:52, 3.83s/it] 70%|███████ | 366/520 [22:55<09:49, 3.83s/it] {'loss': 1.2372, 'grad_norm': 0.000413583807533799, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:55<09:49, 3.83s/it] 71%|███████ | 367/520 [22:59<09:38, 3.78s/it] {'loss': 1.2304, 'grad_norm': 0.000448137677936264, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:59<09:38, 3.78s/it] 71%|███████ | 368/520 [23:03<09:27, 3.74s/it] {'loss': 1.0826, 'grad_norm': 0.0004349219024216966, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:03<09:27, 3.74s/it] 71%|███████ | 369/520 [23:06<09:19, 3.71s/it] {'loss': 1.1754, 'grad_norm': 0.0003938531920501268, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:06<09:19, 3.71s/it] 71%|███████ | 370/520 [23:10<09:11, 3.68s/it] {'loss': 1.1457, 'grad_norm': 0.0004078634722831532, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:10<09:11, 3.68s/it] 71%|███████▏ | 371/520 [23:13<09:05, 3.66s/it] {'loss': 1.1307, 'grad_norm': 0.0004580057603939774, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:13<09:05, 3.66s/it] 72%|███████▏ | 372/520 [23:17<09:00, 3.65s/it] {'loss': 1.2392, 'grad_norm': 0.00040464064010252166, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:17<09:00, 3.65s/it] 72%|███████▏ | 373/520 [23:21<08:56, 3.65s/it] {'loss': 1.1296, 'grad_norm': 0.0004432397502041381, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:21<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:24<08:51, 3.64s/it] {'loss': 1.2289, 'grad_norm': 0.0004634898919527313, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:24<08:51, 3.64s/it] 72%|███████▏ | 375/520 [23:28<08:47, 3.64s/it] {'loss': 1.1457, 'grad_norm': 0.0004388920935536753, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:28<08:47, 3.64s/it] 72%|███████▏ | 376/520 [23:32<08:41, 3.62s/it] {'loss': 1.2509, 'grad_norm': 0.00041299623952023053, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:32<08:41, 3.62s/it] 72%|███████▎ | 377/520 [23:35<08:37, 3.62s/it] {'loss': 1.1823, 'grad_norm': 0.000464497136150202, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:35<08:37, 3.62s/it] 73%|███████▎ | 378/520 [23:39<08:33, 3.62s/it] {'loss': 1.2465, 'grad_norm': 0.00040455956021074235, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:39<08:33, 3.62s/it] 73%|███████▎ | 379/520 [23:42<08:29, 3.61s/it] {'loss': 1.2073, 'grad_norm': 0.0004003944471617409, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:42<08:29, 3.61s/it] 73%|███████▎ | 380/520 [23:46<08:27, 3.62s/it] {'loss': 1.2206, 'grad_norm': 0.000423565172325566, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:46<08:27, 3.62s/it] 73%|███████▎ | 381/520 [23:50<08:30, 3.67s/it] {'loss': 1.2215, 'grad_norm': 0.0004114160419732479, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:50<08:30, 3.67s/it] 73%|███████▎ | 382/520 [23:54<08:34, 3.73s/it] {'loss': 1.1928, 'grad_norm': 0.0003850352460128374, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:54<08:34, 3.73s/it] 74%|███████▎ | 383/520 [23:57<08:34, 3.76s/it] {'loss': 1.0661, 'grad_norm': 0.0004737212477198803, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:57<08:34, 3.76s/it] 74%|███████▍ | 384/520 [24:01<08:34, 3.78s/it] {'loss': 1.2056, 'grad_norm': 0.0003665827863719953, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:01<08:34, 3.78s/it] 74%|███████▍ | 385/520 [24:05<08:35, 3.82s/it] {'loss': 1.211, 'grad_norm': 0.00040352704309614096, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:05<08:35, 3.82s/it] 74%|███████▍ | 386/520 [24:09<08:32, 3.83s/it] {'loss': 1.158, 'grad_norm': 0.00036986315571352715, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:09<08:32, 3.83s/it] 74%|███████▍ | 387/520 [24:13<08:29, 3.83s/it] {'loss': 1.236, 'grad_norm': 0.00040936066013343354, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:13<08:29, 3.83s/it] 75%|███████▍ | 388/520 [24:17<08:25, 3.83s/it] {'loss': 1.122, 'grad_norm': 0.0004178766366719182, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:17<08:25, 3.83s/it] 75%|███████▍ | 389/520 [24:20<08:17, 3.80s/it] {'loss': 1.1686, 'grad_norm': 0.0005679484908283058, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:20<08:17, 3.80s/it] 75%|███████▌ | 390/520 [24:24<08:08, 3.76s/it] {'loss': 1.2366, 'grad_norm': 0.0004040338329434318, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:24<08:08, 3.76s/it] 75%|███████▌ | 391/520 [24:28<08:02, 3.74s/it] {'loss': 1.2916, 'grad_norm': 0.00043186091785239094, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:28<08:02, 3.74s/it] 75%|███████▌ | 392/520 [24:31<07:55, 3.71s/it] {'loss': 1.1211, 'grad_norm': 0.0004251981997473686, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:31<07:55, 3.71s/it] 76%|███████▌ | 393/520 [24:35<07:49, 3.70s/it] {'loss': 1.1038, 'grad_norm': 0.00035532619590887044, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:35<07:49, 3.70s/it] 76%|███████▌ | 394/520 [24:39<07:44, 3.69s/it] {'loss': 1.1938, 'grad_norm': 0.0004440540484248277, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:39<07:44, 3.69s/it] 76%|███████▌ | 395/520 [24:42<07:40, 3.69s/it] {'loss': 1.1594, 'grad_norm': 0.0004587851129262217, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:42<07:40, 3.69s/it] 76%|███████▌ | 396/520 [24:46<07:35, 3.67s/it] {'loss': 1.2308, 'grad_norm': 0.000449597666750154, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:46<07:35, 3.67s/it] 76%|███████▋ | 397/520 [24:50<07:30, 3.66s/it] {'loss': 1.2076, 'grad_norm': 0.0004146835516639494, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:50<07:30, 3.66s/it] 77%|███████▋ | 398/520 [24:53<07:26, 3.66s/it] {'loss': 1.1991, 'grad_norm': 0.00044986874943046996, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:53<07:26, 3.66s/it] 77%|███████▋ | 399/520 [24:57<07:23, 3.67s/it] {'loss': 1.1309, 'grad_norm': 0.00041479380180107036, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:57<07:23, 3.67s/it] 77%|███████▋ | 400/520 [25:01<07:19, 3.66s/it] {'loss': 1.1655, 'grad_norm': 0.0003826972312475355, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [25:01<07:19, 3.66s/it] 77%|███████▋ | 401/520 [25:04<07:14, 3.65s/it] {'loss': 1.0448, 'grad_norm': 0.0004766586535689322, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:04<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:08<07:10, 3.65s/it] {'loss': 1.1752, 'grad_norm': 0.00044280675477904587, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:08<07:10, 3.65s/it] 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] {'loss': 1.1942, 'grad_norm': 0.000462365389342777, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:12<07:06, 3.64s/it] 78%|███████▊ | 404/520 [25:15<07:01, 3.64s/it] {'loss': 1.1107, 'grad_norm': 0.0005002876999661251, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:15<07:01, 3.64s/it] 78%|███████▊ | 405/520 [25:19<06:58, 3.64s/it] {'loss': 1.1481, 'grad_norm': 0.0003991905683268731, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:19<06:58, 3.64s/it] 78%|███████▊ | 406/520 [25:23<06:55, 3.64s/it] {'loss': 1.0747, 'grad_norm': 0.0005105068348267653, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:23<06:55, 3.64s/it] 78%|███████▊ | 407/520 [25:26<06:50, 3.63s/it] {'loss': 1.2692, 'grad_norm': 0.00043072747436325495, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:26<06:50, 3.63s/it] 78%|███████▊ | 408/520 [25:30<06:47, 3.64s/it] {'loss': 1.1897, 'grad_norm': 0.00047965756551948956, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:30<06:47, 3.64s/it] 79%|███████▊ | 409/520 [25:33<06:42, 3.63s/it] {'loss': 1.3039, 'grad_norm': 0.00045839999298743583, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:33<06:42, 3.63s/it] 79%|███████▉ | 410/520 [25:37<06:39, 3.63s/it] {'loss': 1.0466, 'grad_norm': 0.0004415656645605262, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:37<06:39, 3.63s/it] 79%|███████▉ | 411/520 [25:41<06:35, 3.63s/it] {'loss': 1.2852, 'grad_norm': 0.00046405429634237706, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:41<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:44<06:32, 3.63s/it] {'loss': 1.1965, 'grad_norm': 0.0004199022243666374, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:44<06:32, 3.63s/it] 79%|███████▉ | 413/520 [25:48<06:29, 3.64s/it] {'loss': 1.167, 'grad_norm': 0.0004053917143850689, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:48<06:29, 3.64s/it] 80%|███████▉ | 414/520 [25:52<06:25, 3.63s/it] {'loss': 0.9762, 'grad_norm': 0.00034985795305627574, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:52<06:25, 3.63s/it] 80%|███████▉ | 415/520 [25:55<06:21, 3.63s/it] {'loss': 1.1777, 'grad_norm': 0.0004098875808611526, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:55<06:21, 3.63s/it] 80%|████████ | 416/520 [25:59<06:17, 3.63s/it] {'loss': 1.0795, 'grad_norm': 0.0004835460868978607, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:59<06:17, 3.63s/it] 80%|████████ | 417/520 [26:03<06:14, 3.64s/it] {'loss': 1.2405, 'grad_norm': 0.00042292224275012865, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:03<06:14, 3.64s/it] 80%|████████ | 418/520 [26:06<06:10, 3.63s/it] {'loss': 1.2373, 'grad_norm': 0.000402005141868115, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:06<06:10, 3.63s/it] 81%|████████ | 419/520 [26:10<06:07, 3.63s/it] {'loss': 1.2325, 'grad_norm': 0.00046787283787808666, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:10<06:07, 3.63s/it] 81%|████████ | 420/520 [26:13<06:03, 3.64s/it] {'loss': 1.1214, 'grad_norm': 0.0004457823468354941, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:13<06:03, 3.64s/it] 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] {'loss': 1.0609, 'grad_norm': 0.00045764125730804277, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:17<06:00, 3.64s/it] 81%|████████ | 422/520 [26:21<05:56, 3.64s/it] {'loss': 1.1841, 'grad_norm': 0.00045238963431887177, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:21<05:56, 3.64s/it] 81%|████████▏ | 423/520 [26:24<05:52, 3.64s/it] {'loss': 1.1487, 'grad_norm': 0.000462181163953593, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:24<05:52, 3.64s/it] 82%|████████▏ | 424/520 [26:28<05:50, 3.65s/it] {'loss': 1.245, 'grad_norm': 0.000393790311815413, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:28<05:50, 3.65s/it] 82%|████████▏ | 425/520 [26:32<05:46, 3.65s/it] {'loss': 1.1629, 'grad_norm': 0.0004294748594994811, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:32<05:46, 3.65s/it] 82%|████████▏ | 426/520 [26:35<05:42, 3.65s/it] {'loss': 1.2035, 'grad_norm': 0.000558274845038215, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:35<05:42, 3.65s/it] 82%|████████▏ | 427/520 [26:39<05:39, 3.65s/it] {'loss': 1.0949, 'grad_norm': 0.00040703964516658616, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:39<05:39, 3.65s/it] 82%|████████▏ | 428/520 [26:43<05:36, 3.65s/it] {'loss': 1.0937, 'grad_norm': 0.0004661051981132951, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:43<05:36, 3.65s/it] 82%|████████▎ | 429/520 [26:46<05:32, 3.66s/it] {'loss': 1.1925, 'grad_norm': 0.00043777063138252106, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:46<05:32, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:50<05:29, 3.66s/it] {'loss': 1.1932, 'grad_norm': 0.0004179982985594796, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:50<05:29, 3.66s/it] 83%|████████▎ | 431/520 [26:54<05:26, 3.67s/it] {'loss': 1.1373, 'grad_norm': 0.00041826129167380784, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:54<05:26, 3.67s/it] 83%|████████▎ | 432/520 [26:57<05:22, 3.66s/it] {'loss': 1.0968, 'grad_norm': 0.00045654343941478603, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:57<05:22, 3.66s/it] 83%|████████▎ | 433/520 [27:01<05:18, 3.66s/it] {'loss': 1.2307, 'grad_norm': 0.0004414263025710044, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:01<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:05<05:14, 3.66s/it] {'loss': 0.9873, 'grad_norm': 0.000443681778441274, 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{'loss': 1.28, 'grad_norm': 0.0003777912723298001, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:34<00:47, 3.64s/it] 98%|█████████▊| 508/520 [31:37<00:43, 3.63s/it] {'loss': 1.277, 'grad_norm': 0.0004402379121948081, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:37<00:43, 3.63s/it] 98%|█████████▊| 509/520 [31:41<00:40, 3.64s/it] {'loss': 1.245, 'grad_norm': 0.0004218845871099459, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:41<00:40, 3.64s/it] 98%|█████████▊| 510/520 [31:45<00:36, 3.66s/it] {'loss': 1.1959, 'grad_norm': 0.00043157812076212774, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:45<00:36, 3.66s/it] 98%|█████████▊| 511/520 [31:48<00:32, 3.64s/it] {'loss': 1.1541, 'grad_norm': 0.00041825863247018426, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:48<00:32, 3.64s/it] 98%|█████████▊| 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[32:06<00:14, 3.62s/it] 99%|█████████▉| 517/520 [32:10<00:10, 3.61s/it] {'loss': 1.1782, 'grad_norm': 0.0003963564877718761, 'learning_rate': 1.7483908725357544e-05, 'epoch': 0.99} + 99%|█████████▉| 517/520 [32:10<00:10, 3.61s/it] 100%|█████████▉| 518/520 [32:14<00:07, 3.59s/it] {'loss': 1.1829, 'grad_norm': 0.00045004099447612034, 'learning_rate': 7.770751902513862e-06, 'epoch': 1.0} + 100%|█████████▉| 518/520 [32:14<00:07, 3.59s/it] 100%|█████████▉| 519/520 [32:17<00:03, 3.58s/it] {'loss': 1.1574, 'grad_norm': 0.00041630126761558334, 'learning_rate': 1.9427068461808086e-06, 'epoch': 1.0} + 100%|█████████▉| 519/520 [32:17<00:03, 3.58s/it] 100%|██████████| 520/520 [32:22<00:00, 3.87s/it] {'loss': 1.1411, 'grad_norm': 0.000369922634888921, 'learning_rate': 0.0, 'epoch': 1.0} + 100%|██████████| 520/520 [32:22<00:00, 3.87s/it] {'train_runtime': 1942.1848, 'train_samples_per_second': 34.255, 'train_steps_per_second': 0.268, 'train_loss': 1.2330138058616564, 'epoch': 1.0} + 100%|██████████| 520/520 [32:22<00:00, 3.87s/it] 100%|██████████| 520/520 [32:22<00:00, 3.73s/it] +[2025-10-18 03:38:53,241] [INFO] [launch.py:348:main] Process 898643 exits successfully. +[2025-10-18 03:38:54,242] [INFO] [launch.py:348:main] Process 898637 exits successfully. +[2025-10-18 03:38:54,242] [INFO] [launch.py:348:main] Process 898639 exits successfully. +[2025-10-18 03:38:54,243] [INFO] [launch.py:348:main] Process 898641 exits successfully. +[2025-10-18 03:38:54,243] [INFO] [launch.py:348:main] Process 898640 exits successfully. +[2025-10-18 03:38:55,245] [INFO] [launch.py:348:main] Process 898642 exits successfully. +[2025-10-18 03:38:55,245] [INFO] [launch.py:348:main] Process 898638 exits successfully. +[2025-10-18 03:38:58,249] [INFO] [launch.py:348:main] Process 898636 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_1.9_2e-1_connector-9.0_1.9_2e-1_ablation_20251018_030457.log +Timestamp: 2025-10-18 03:39:00 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation_20251018_033900.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation_20251018_033900.log new file mode 100644 index 0000000000000000000000000000000000000000..11d4c935fc2bea7906f479da56eee8ddf5b88449 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation_20251018_033900.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation_20251018_033900.log +Timestamp: 2025-10-18 03:39:00 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:39:03,470] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:06,465] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 03:39:06,467] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 2.1 --temperature_mlp_text 2.1 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 2.1 --temperature_mlp_vision 2.1 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 2.1 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:39:09,029] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:10,098] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 03:39:10,098] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 03:39:10,099] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 03:39:10,099] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 03:39:10,099] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 03:39:10,099] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 03:39:10,099] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 03:39:10,101] [INFO] [launch.py:253:main] process 920995 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,103] [INFO] [launch.py:253:main] process 920996 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,105] [INFO] [launch.py:253:main] process 920997 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,106] [INFO] [launch.py:253:main] process 920998 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,108] [INFO] [launch.py:253:main] process 920999 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,110] [INFO] [launch.py:253:main] process 921000 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,112] [INFO] [launch.py:253:main] process 921001 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 03:39:10,114] [INFO] [launch.py:253:main] process 921002 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.1', '--temperature_mlp_text', '2.1', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.1', '--temperature_mlp_vision', '2.1', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.1', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 03:39:16,799] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,015] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,018] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,054] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,106] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,106] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,106] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,107] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 03:39:17,213] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,440] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,440] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 03:39:17,442] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,453] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,519] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,521] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,526] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 03:39:17,527] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.1, 'temperature_mlp': 2.1, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.1, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.1, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.1, + "temperature_mlp": 2.1, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:920995:920995 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:921001:921001 [6] NCCL INFO NET/Plugin: Using internal network plugin. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:921002:921002 [7] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:921002:922587 [7] NCCL INFO ncclCommInitRank comm 0x55a867628d80 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:920999:922588 [4] NCCL INFO ncclCommInitRank comm 0x56392b72c320 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:920998:922589 [3] NCCL INFO ncclCommInitRank comm 0x5583f7008740 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:920995:922567 [0] NCCL INFO ncclCommInitRank comm 0x55bdb6658590 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:920996:922586 [1] NCCL INFO ncclCommInitRank comm 0x561abfd86270 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:920997:922590 [2] NCCL INFO ncclCommInitRank comm 0x55c991db18a0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:921001:922568 [6] NCCL INFO ncclCommInitRank comm 0x55c5dab41550 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xc02e6701803fcea5 - Init COMPLETE +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:921000:922585 [5] NCCL INFO ncclCommInitRank comm 0x5614d1c16630 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xc02e6701803fcea5 - Init COMPLETE +[2025-10-18 03:40:04,591] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 03:40:06,309] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 03:40:27,253 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 03:40:27,258 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters 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4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters 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4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters 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+language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:005->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read 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15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read 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18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read 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20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:920995:927604 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920997:927607 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921002:927611 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920999:927606 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920996:927605 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:920998:927608 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921000:927610 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:921001:927609 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<43:37, 5.07s/it] 1%| | 5/520 [00:28<38:56, 4.54s/it] {'loss': 1.6661, 'grad_norm': 0.0009599991489849139, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<38:56, 4.54s/it] 1%| | 6/520 [00:32<36:09, 4.22s/it] {'loss': 1.3817, 'grad_norm': 0.00042010311628217905, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:09, 4.22s/it] 1%|▏ | 7/520 [00:35<34:22, 4.02s/it] {'loss': 1.4699, 'grad_norm': 0.0005123296426389964, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:35<34:22, 4.02s/it] 2%|▏ | 8/520 [00:40<34:50, 4.08s/it] {'loss': 1.5012, 'grad_norm': 0.00042193714622352894, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<34:50, 4.08s/it] 2%|▏ | 9/520 [00:44<34:51, 4.09s/it] {'loss': 1.5757, 'grad_norm': 0.00043181969515775764, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<34:51, 4.09s/it] 2%|▏ | 10/520 [00:47<33:28, 3.94s/it] {'loss': 1.4218, 'grad_norm': 0.00042425700695650193, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:47<33:28, 3.94s/it] 2%|▏ | 11/520 [00:51<32:59, 3.89s/it] {'loss': 1.4628, 'grad_norm': 0.0003813287567779885, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:51<32:59, 3.89s/it] 2%|▏ | 12/520 [00:55<32:09, 3.80s/it] {'loss': 1.3427, 'grad_norm': 0.00036185254720931905, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<32:09, 3.80s/it][2025-10-18 03:41:31,241] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:59<33:23, 3.95s/it] {'loss': 1.4229, 'grad_norm': 0.0003303330210816195, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:59<33:23, 3.95s/it] 3%|▎ | 14/520 [01:03<32:25, 3.84s/it] {'loss': 1.4541, 'grad_norm': 0.0003832216232201816, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:03<32:25, 3.84s/it] 3%|▎ | 15/520 [01:06<31:59, 3.80s/it] {'loss': 1.374, 'grad_norm': 0.00030637394763343437, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:06<31:59, 3.80s/it] 3%|▎ | 16/520 [01:10<31:22, 3.74s/it] {'loss': 1.355, 'grad_norm': 0.00040379311306131004, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:10<31:22, 3.74s/it] 3%|▎ | 17/520 [01:14<31:15, 3.73s/it] {'loss': 1.4874, 'grad_norm': 0.00042838503464413585, 'learning_rate': 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0.00031496610323812255, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:56<31:48, 3.88s/it] 6%|▌ | 29/520 [02:00<31:48, 3.89s/it] {'loss': 1.3134, 'grad_norm': 0.00029577029517412895, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [02:00<31:48, 3.89s/it] 6%|▌ | 30/520 [02:04<31:10, 3.82s/it] {'loss': 1.3784, 'grad_norm': 0.0002947951543664974, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:04<31:10, 3.82s/it] 6%|▌ | 31/520 [02:08<31:01, 3.81s/it] {'loss': 1.2791, 'grad_norm': 0.0002897010775380896, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:08<31:01, 3.81s/it] 6%|▌ | 32/520 [02:12<31:15, 3.84s/it] {'loss': 1.1984, 'grad_norm': 0.0002792824938583382, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:12<31:15, 3.84s/it] 6%|▋ | 33/520 [02:15<31:16, 3.85s/it] {'loss': 1.2727, 'grad_norm': 0.0003046143601801695, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 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[18:46<13:11, 3.62s/it] 58%|█████▊ | 302/520 [18:50<13:08, 3.62s/it] {'loss': 1.214, 'grad_norm': 0.00038284458549720483, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:50<13:08, 3.62s/it] 58%|█████▊ | 303/520 [18:53<13:05, 3.62s/it] {'loss': 1.1799, 'grad_norm': 0.00048213720359278623, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:53<13:05, 3.62s/it] 58%|█████▊ | 304/520 [18:57<13:02, 3.62s/it] {'loss': 1.1333, 'grad_norm': 0.00046247470079430246, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [18:57<13:02, 3.62s/it] 59%|█████▊ | 305/520 [19:00<12:56, 3.61s/it] {'loss': 1.2784, 'grad_norm': 0.00047391306465565405, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:00<12:56, 3.61s/it] 59%|█████▉ | 306/520 [19:04<12:52, 3.61s/it] {'loss': 1.2287, 'grad_norm': 0.00044090884994599683, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:04<12:52, 3.61s/it] 59%|█████▉ | 307/520 [19:08<13:08, 3.70s/it] {'loss': 1.1719, 'grad_norm': 0.00039844261121630586, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:08<13:08, 3.70s/it] 59%|█████▉ | 308/520 [19:12<12:57, 3.67s/it] {'loss': 1.286, 'grad_norm': 0.0004681033968838937, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:12<12:57, 3.67s/it] 59%|█████▉ | 309/520 [19:15<12:50, 3.65s/it] {'loss': 1.1811, 'grad_norm': 0.0004122388165002733, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:15<12:50, 3.65s/it] 60%|█████▉ | 310/520 [19:19<12:43, 3.63s/it] {'loss': 1.1564, 'grad_norm': 0.0004118135772261234, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:19<12:43, 3.63s/it] 60%|█████▉ | 311/520 [19:22<12:37, 3.62s/it] {'loss': 1.146, 'grad_norm': 0.00043787370512544705, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:22<12:37, 3.62s/it] 60%|██████ | 312/520 [19:26<12:32, 3.62s/it] {'loss': 1.1314, 'grad_norm': 0.00042391735278573926, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:26<12:32, 3.62s/it] 60%|██████ | 313/520 [19:30<12:29, 3.62s/it] {'loss': 1.1053, 'grad_norm': 0.00039874538098711354, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:30<12:29, 3.62s/it] 60%|██████ | 314/520 [19:34<12:56, 3.77s/it] {'loss': 1.1494, 'grad_norm': 0.00039702773051460903, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:34<12:56, 3.77s/it] 61%|██████ | 315/520 [19:37<12:43, 3.73s/it] {'loss': 1.1783, 'grad_norm': 0.00046944636605236517, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:37<12:43, 3.73s/it] 61%|██████ | 316/520 [19:41<13:01, 3.83s/it] {'loss': 1.139, 'grad_norm': 0.0004415752697822305, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:41<13:01, 3.83s/it] 61%|██████ | 317/520 [19:45<12:43, 3.76s/it] {'loss': 1.1343, 'grad_norm': 0.00038254904601468397, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:45<12:43, 3.76s/it] 61%|██████ | 318/520 [19:49<12:32, 3.73s/it] {'loss': 1.2465, 'grad_norm': 0.0004334942072818082, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:49<12:32, 3.73s/it] 61%|██████▏ | 319/520 [19:53<12:43, 3.80s/it] {'loss': 1.1321, 'grad_norm': 0.00037290332786402847, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:53<12:43, 3.80s/it] 62%|██████▏ | 320/520 [19:56<12:29, 3.75s/it] {'loss': 1.0698, 'grad_norm': 0.0004352616297948359, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:56<12:29, 3.75s/it] 62%|██████▏ | 321/520 [20:00<12:22, 3.73s/it] {'loss': 1.2681, 'grad_norm': 0.0004346315471040477, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:00<12:22, 3.73s/it] 62%|██████▏ | 322/520 [20:04<12:16, 3.72s/it] {'loss': 1.0812, 'grad_norm': 0.0003902477219102425, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:04<12:16, 3.72s/it] 62%|██████▏ | 323/520 [20:07<12:05, 3.68s/it] {'loss': 1.1554, 'grad_norm': 0.00040175577941814613, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:07<12:05, 3.68s/it] 62%|██████▏ | 324/520 [20:11<11:59, 3.67s/it] {'loss': 1.2143, 'grad_norm': 0.0004149992532285869, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:11<11:59, 3.67s/it] 62%|██████▎ | 325/520 [20:15<11:52, 3.65s/it] {'loss': 1.206, 'grad_norm': 0.0004393018740862387, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:15<11:52, 3.65s/it] 63%|██████▎ | 326/520 [20:18<11:46, 3.64s/it] {'loss': 1.2108, 'grad_norm': 0.00044165011115080566, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:18<11:46, 3.64s/it] 63%|██████▎ | 327/520 [20:22<11:39, 3.62s/it] {'loss': 1.1823, 'grad_norm': 0.000443292425803593, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:22<11:39, 3.62s/it] 63%|██████▎ | 328/520 [20:25<11:35, 3.62s/it] {'loss': 1.2455, 'grad_norm': 0.0004269867710193414, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:25<11:35, 3.62s/it] 63%|██████▎ | 329/520 [20:29<11:32, 3.63s/it] {'loss': 1.1324, 'grad_norm': 0.0003639959441927618, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:29<11:32, 3.63s/it] 63%|██████▎ | 330/520 [20:33<11:28, 3.62s/it] {'loss': 1.2092, 'grad_norm': 0.00040105606677053496, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:33<11:28, 3.62s/it] 64%|██████▎ | 331/520 [20:36<11:22, 3.61s/it] {'loss': 1.1662, 'grad_norm': 0.00043384591453439535, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:36<11:22, 3.61s/it] 64%|██████▍ | 332/520 [20:40<11:20, 3.62s/it] {'loss': 1.2125, 'grad_norm': 0.0003727973119587566, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:40<11:20, 3.62s/it] 64%|██████▍ | 333/520 [20:43<11:17, 3.62s/it] {'loss': 1.3006, 'grad_norm': 0.00043169183280706826, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:43<11:17, 3.62s/it] 64%|██████▍ | 334/520 [20:47<11:13, 3.62s/it] {'loss': 1.2121, 'grad_norm': 0.0004441349790468909, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:47<11:13, 3.62s/it] 64%|██████▍ | 335/520 [20:51<11:09, 3.62s/it] {'loss': 1.2146, 'grad_norm': 0.0004243671599952173, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:51<11:09, 3.62s/it] 65%|██████▍ | 336/520 [20:54<11:08, 3.63s/it] {'loss': 1.124, 'grad_norm': 0.0004558815677928904, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:54<11:08, 3.63s/it] 65%|██████▍ | 337/520 [20:58<11:03, 3.62s/it] {'loss': 1.1157, 'grad_norm': 0.00043185534600383434, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [20:58<11:03, 3.62s/it] 65%|██████▌ | 338/520 [21:02<11:00, 3.63s/it] {'loss': 1.2197, 'grad_norm': 0.00040825149438594645, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:02<11:00, 3.63s/it] 65%|██████▌ | 339/520 [21:05<10:57, 3.63s/it] {'loss': 1.1611, 'grad_norm': 0.0004633232232826895, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:05<10:57, 3.63s/it] 65%|██████▌ | 340/520 [21:09<10:55, 3.64s/it] {'loss': 1.1494, 'grad_norm': 0.0004211007319657125, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:09<10:55, 3.64s/it] 66%|██████▌ | 341/520 [21:13<10:50, 3.64s/it] {'loss': 1.1836, 'grad_norm': 0.00044914377978918694, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:13<10:50, 3.64s/it] 66%|██████▌ | 342/520 [21:16<10:46, 3.63s/it] {'loss': 1.1921, 'grad_norm': 0.00047510515069700286, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:16<10:46, 3.63s/it] 66%|██████▌ | 343/520 [21:20<10:44, 3.64s/it] {'loss': 1.1371, 'grad_norm': 0.0003735175949741504, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:20<10:44, 3.64s/it] 66%|██████▌ | 344/520 [21:23<10:41, 3.64s/it] {'loss': 1.1401, 'grad_norm': 0.0003920434196190251, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:23<10:41, 3.64s/it] 66%|██████▋ | 345/520 [21:27<10:35, 3.63s/it] {'loss': 1.2372, 'grad_norm': 0.00042796317515066313, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:27<10:35, 3.63s/it] 67%|██████▋ | 346/520 [21:31<10:32, 3.64s/it] {'loss': 1.1587, 'grad_norm': 0.0004357234534772268, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:31<10:32, 3.64s/it] 67%|██████▋ | 347/520 [21:34<10:28, 3.64s/it] {'loss': 1.1569, 'grad_norm': 0.0003815757001060356, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:34<10:28, 3.64s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:38<10:24, 3.63s/it] {'loss': 1.1116, 'grad_norm': 0.000511425388397689, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:38<10:24, 3.63s/it] 67%|██████▋ | 349/520 [21:42<10:20, 3.63s/it] {'loss': 1.1471, 'grad_norm': 0.0004158376839341179, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:42<10:20, 3.63s/it] 67%|██████▋ | 350/520 [21:45<10:17, 3.63s/it] {'loss': 1.1923, 'grad_norm': 0.0004825537315825019, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:45<10:17, 3.63s/it] 68%|██████▊ | 351/520 [21:49<10:14, 3.64s/it] {'loss': 1.1061, 'grad_norm': 0.00041685650869947323, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:49<10:14, 3.64s/it] 68%|██████▊ | 352/520 [21:52<10:10, 3.63s/it] {'loss': 1.2157, 'grad_norm': 0.0003861347837307188, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:52<10:10, 3.63s/it] 68%|██████▊ | 353/520 [21:56<10:07, 3.64s/it] {'loss': 1.1363, 'grad_norm': 0.00036440650675803333, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:56<10:07, 3.64s/it] 68%|██████▊ | 354/520 [22:00<10:03, 3.64s/it] {'loss': 1.2233, 'grad_norm': 0.00042580168422330964, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:00<10:03, 3.64s/it] 68%|██████▊ | 355/520 [22:03<09:59, 3.63s/it] {'loss': 1.1693, 'grad_norm': 0.00042450284066700947, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:03<09:59, 3.63s/it] 68%|██████▊ | 356/520 [22:07<09:55, 3.63s/it] {'loss': 1.1675, 'grad_norm': 0.0004254401055082916, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:07<09:55, 3.63s/it] 69%|██████▊ | 357/520 [22:11<09:49, 3.62s/it] {'loss': 1.2046, 'grad_norm': 0.0003950173583969398, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:11<09:49, 3.62s/it] 69%|██████▉ | 358/520 [22:14<09:48, 3.63s/it] {'loss': 1.1309, 'grad_norm': 0.0004134525518373981, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:14<09:48, 3.63s/it] 69%|██████▉ | 359/520 [22:18<09:54, 3.69s/it] {'loss': 1.1674, 'grad_norm': 0.00041318172586526746, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:18<09:54, 3.69s/it] 69%|██████▉ | 360/520 [22:22<09:56, 3.73s/it] {'loss': 1.1732, 'grad_norm': 0.00046157827885205563, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:22<09:56, 3.73s/it] 69%|██████▉ | 361/520 [22:26<09:57, 3.76s/it] {'loss': 1.1946, 'grad_norm': 0.0003857083233722857, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:26<09:57, 3.76s/it] 70%|██████▉ | 362/520 [22:30<09:59, 3.79s/it] {'loss': 1.177, 'grad_norm': 0.0004537399920381868, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:30<09:59, 3.79s/it] 70%|██████▉ | 363/520 [22:33<09:57, 3.80s/it] {'loss': 1.2073, 'grad_norm': 0.00040524783754596164, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:33<09:57, 3.80s/it] 70%|███████ | 364/520 [22:37<09:54, 3.81s/it] {'loss': 1.2039, 'grad_norm': 0.00040557929847536003, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:37<09:54, 3.81s/it] 70%|███████ | 365/520 [22:41<09:51, 3.81s/it] {'loss': 1.2591, 'grad_norm': 0.0004196942632105813, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:41<09:51, 3.81s/it] 70%|███████ | 366/520 [22:45<09:41, 3.77s/it] {'loss': 1.2299, 'grad_norm': 0.0003977273294683592, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:45<09:41, 3.77s/it] 71%|███████ | 367/520 [22:48<09:30, 3.73s/it] {'loss': 1.2242, 'grad_norm': 0.00043951630182954864, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:48<09:30, 3.73s/it] 71%|███████ | 368/520 [22:52<09:23, 3.71s/it] {'loss': 1.0775, 'grad_norm': 0.0004195121096083589, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:52<09:23, 3.71s/it] 71%|███████ | 369/520 [22:56<09:18, 3.70s/it] {'loss': 1.1702, 'grad_norm': 0.0003915090061340679, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:56<09:18, 3.70s/it] 71%|███████ | 370/520 [22:59<09:13, 3.69s/it] {'loss': 1.1392, 'grad_norm': 0.0003876554011307542, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [22:59<09:13, 3.69s/it] 71%|███████▏ | 371/520 [23:03<09:09, 3.69s/it] {'loss': 1.1273, 'grad_norm': 0.0004490933108877204, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:03<09:09, 3.69s/it] 72%|███████▏ | 372/520 [23:07<09:01, 3.66s/it] {'loss': 1.2337, 'grad_norm': 0.0004346087086185619, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:07<09:01, 3.66s/it] 72%|███████▏ | 373/520 [23:10<08:56, 3.65s/it] {'loss': 1.124, 'grad_norm': 0.00042380581445689773, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:10<08:56, 3.65s/it] 72%|███████▏ | 374/520 [23:14<08:51, 3.64s/it] {'loss': 1.223, 'grad_norm': 0.0004524649945391989, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:14<08:51, 3.64s/it] 72%|███████▏ | 375/520 [23:18<08:46, 3.63s/it] {'loss': 1.1388, 'grad_norm': 0.0004216280231499845, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:18<08:46, 3.63s/it] 72%|███████▏ | 376/520 [23:21<08:43, 3.64s/it] {'loss': 1.2456, 'grad_norm': 0.00039677248870180167, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:21<08:43, 3.64s/it] 72%|███████▎ | 377/520 [23:25<08:39, 3.63s/it] {'loss': 1.1749, 'grad_norm': 0.00043669280558570375, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:25<08:39, 3.63s/it] 73%|███████▎ | 378/520 [23:28<08:34, 3.63s/it] {'loss': 1.2405, 'grad_norm': 0.00038732110593070094, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:28<08:34, 3.63s/it] 73%|███████▎ | 379/520 [23:32<08:30, 3.62s/it] {'loss': 1.2024, 'grad_norm': 0.00038645604786236135, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:32<08:30, 3.62s/it] 73%|███████▎ | 380/520 [23:36<08:26, 3.62s/it] {'loss': 1.2152, 'grad_norm': 0.0004115559927523996, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:36<08:26, 3.62s/it] 73%|███████▎ | 381/520 [23:39<08:23, 3.63s/it] {'loss': 1.2159, 'grad_norm': 0.0003937694836000651, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:39<08:23, 3.63s/it] 73%|███████▎ | 382/520 [23:43<08:21, 3.63s/it] {'loss': 1.187, 'grad_norm': 0.0003746652185124093, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:43<08:21, 3.63s/it] 74%|███████▎ | 383/520 [23:47<08:16, 3.63s/it] {'loss': 1.0591, 'grad_norm': 0.00046272945768073385, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:47<08:16, 3.63s/it] 74%|███████▍ | 384/520 [23:50<08:12, 3.62s/it] {'loss': 1.2012, 'grad_norm': 0.0003661931083254156, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:50<08:12, 3.62s/it] 74%|███████▍ | 385/520 [23:54<08:09, 3.63s/it] {'loss': 1.2028, 'grad_norm': 0.00038284639781049565, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:54<08:09, 3.63s/it] 74%|███████▍ | 386/520 [23:57<08:05, 3.63s/it] {'loss': 1.1528, 'grad_norm': 0.0003536503637963802, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:57<08:05, 3.63s/it] 74%|███████▍ | 387/520 [24:01<08:08, 3.67s/it] {'loss': 1.2318, 'grad_norm': 0.0003955366328947489, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:01<08:08, 3.67s/it] 75%|███████▍ | 388/520 [24:05<08:03, 3.66s/it] {'loss': 1.1145, 'grad_norm': 0.0003992456861749586, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:05<08:03, 3.66s/it] 75%|███████▍ | 389/520 [24:08<07:58, 3.65s/it] {'loss': 1.1612, 'grad_norm': 0.0005561641079325422, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:08<07:58, 3.65s/it] 75%|███████▌ | 390/520 [24:12<07:54, 3.65s/it] {'loss': 1.23, 'grad_norm': 0.00039994618330815515, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:12<07:54, 3.65s/it] 75%|███████▌ | 391/520 [24:16<07:50, 3.65s/it] {'loss': 1.2853, 'grad_norm': 0.00043058789433551327, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:16<07:50, 3.65s/it] 75%|███████▌ | 392/520 [24:19<07:47, 3.65s/it] {'loss': 1.1141, 'grad_norm': 0.0004265389723653727, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:19<07:47, 3.65s/it] 76%|███████▌ | 393/520 [24:23<07:42, 3.64s/it] {'loss': 1.0992, 'grad_norm': 0.0003414313948098156, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:23<07:42, 3.64s/it] 76%|███████▌ | 394/520 [24:27<07:37, 3.63s/it] {'loss': 1.1868, 'grad_norm': 0.0004292666083307023, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:27<07:37, 3.63s/it] 76%|███████▌ | 395/520 [24:30<07:33, 3.63s/it] {'loss': 1.1506, 'grad_norm': 0.00044950845287557026, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:30<07:33, 3.63s/it] 76%|███████▌ | 396/520 [24:34<07:29, 3.62s/it] {'loss': 1.2254, 'grad_norm': 0.0004402487161278023, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:34<07:29, 3.62s/it] 76%|███████▋ | 397/520 [24:38<07:26, 3.63s/it] {'loss': 1.2008, 'grad_norm': 0.000395666810901891, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:38<07:26, 3.63s/it] 77%|███████▋ | 398/520 [24:41<07:24, 3.64s/it] {'loss': 1.1935, 'grad_norm': 0.00043084997310329545, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:41<07:24, 3.64s/it] 77%|███████▋ | 399/520 [24:45<07:21, 3.65s/it] {'loss': 1.1262, 'grad_norm': 0.0004163987023302627, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:45<07:21, 3.65s/it] 77%|███████▋ | 400/520 [24:49<07:18, 3.66s/it] {'loss': 1.1613, 'grad_norm': 0.0003710984060494971, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:49<07:18, 3.66s/it] 77%|███████▋ | 401/520 [24:52<07:19, 3.69s/it] {'loss': 1.0398, 'grad_norm': 0.00047612465945319704, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:52<07:19, 3.69s/it] 77%|███████▋ | 402/520 [24:56<07:14, 3.68s/it] {'loss': 1.1672, 'grad_norm': 0.00042660189897128335, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:56<07:14, 3.68s/it] 78%|███████▊ | 403/520 [25:00<07:08, 3.66s/it] {'loss': 1.1876, 'grad_norm': 0.00044298770649848075, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:00<07:08, 3.66s/it] 78%|███████▊ | 404/520 [25:03<07:03, 3.65s/it] {'loss': 1.1027, 'grad_norm': 0.00048051792849769347, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:03<07:03, 3.65s/it] 78%|███████▊ | 405/520 [25:07<06:58, 3.64s/it] {'loss': 1.1429, 'grad_norm': 0.00038708791602274224, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:07<06:58, 3.64s/it] 78%|███████▊ | 406/520 [25:10<06:54, 3.64s/it] {'loss': 1.0685, 'grad_norm': 0.0005210436648818586, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:10<06:54, 3.64s/it] 78%|███████▊ | 407/520 [25:14<06:50, 3.63s/it] {'loss': 1.2631, 'grad_norm': 0.00042023267712140965, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:14<06:50, 3.63s/it] 78%|███████▊ | 408/520 [25:18<06:45, 3.62s/it] {'loss': 1.1832, 'grad_norm': 0.0004808662264774641, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:18<06:45, 3.62s/it] 79%|███████▊ | 409/520 [25:21<06:40, 3.61s/it] {'loss': 1.2967, 'grad_norm': 0.0004667351247399344, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:21<06:40, 3.61s/it] 79%|███████▉ | 410/520 [25:25<06:37, 3.61s/it] {'loss': 1.0408, 'grad_norm': 0.00043567066248873616, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:25<06:37, 3.61s/it] 79%|███████▉ | 411/520 [25:28<06:33, 3.61s/it] {'loss': 1.2783, 'grad_norm': 0.0004439548178989876, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:28<06:33, 3.61s/it] 79%|███████▉ | 412/520 [25:32<06:30, 3.61s/it] {'loss': 1.1888, 'grad_norm': 0.0004025794888413531, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:32<06:30, 3.61s/it] 79%|███████▉ | 413/520 [25:36<06:26, 3.61s/it] {'loss': 1.1603, 'grad_norm': 0.0004067749514491051, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:36<06:26, 3.61s/it] 80%|███████▉ | 414/520 [25:39<06:23, 3.62s/it] {'loss': 0.9716, 'grad_norm': 0.0003469923266070101, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:39<06:23, 3.62s/it] 80%|███████▉ | 415/520 [25:43<06:18, 3.61s/it] {'loss': 1.1697, 'grad_norm': 0.0003933507187743533, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:43<06:18, 3.61s/it] 80%|████████ | 416/520 [25:46<06:14, 3.60s/it] {'loss': 1.075, 'grad_norm': 0.0004890689639118231, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:47<06:14, 3.60s/it] 80%|████████ | 417/520 [25:50<06:10, 3.59s/it] {'loss': 1.2348, 'grad_norm': 0.00040550473346697124, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:50<06:10, 3.59s/it] 80%|████████ | 418/520 [25:54<06:06, 3.59s/it] {'loss': 1.2311, 'grad_norm': 0.0003931266539130203, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:54<06:06, 3.59s/it] 81%|████████ | 419/520 [25:57<06:02, 3.59s/it] {'loss': 1.2248, 'grad_norm': 0.0004470546579680262, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:57<06:02, 3.59s/it] 81%|████████ | 420/520 [26:01<05:58, 3.58s/it] {'loss': 1.1144, 'grad_norm': 0.0004286609852968032, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:01<05:58, 3.58s/it] 81%|████████ | 421/520 [26:04<05:54, 3.58s/it] {'loss': 1.0544, 'grad_norm': 0.0004446289165245403, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:04<05:54, 3.58s/it] 81%|████████ | 422/520 [26:08<05:49, 3.57s/it] {'loss': 1.1762, 'grad_norm': 0.00043143592840598683, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:08<05:49, 3.57s/it] 81%|████████▏ | 423/520 [26:12<05:47, 3.58s/it] {'loss': 1.1424, 'grad_norm': 0.00044462544991611194, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:12<05:47, 3.58s/it] 82%|████████▏ | 424/520 [26:15<05:45, 3.59s/it] {'loss': 1.2401, 'grad_norm': 0.0004001129931809661, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:15<05:45, 3.59s/it] 82%|████████▏ | 425/520 [26:19<05:40, 3.58s/it] {'loss': 1.1566, 'grad_norm': 0.0004206519529010939, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:19<05:40, 3.58s/it] 82%|████████▏ | 426/520 [26:22<05:36, 3.58s/it] {'loss': 1.1951, 'grad_norm': 0.000533463970459392, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:22<05:36, 3.58s/it] 82%|████████▏ | 427/520 [26:26<05:33, 3.58s/it] {'loss': 1.0898, 'grad_norm': 0.0003907246439871366, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:26<05:33, 3.58s/it] 82%|████████▏ | 428/520 [26:29<05:29, 3.58s/it] {'loss': 1.0861, 'grad_norm': 0.0004474162939840453, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:29<05:29, 3.58s/it] 82%|████████▎ | 429/520 [26:33<05:26, 3.59s/it] {'loss': 1.1851, 'grad_norm': 0.00043297216597086655, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:33<05:26, 3.59s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:37<05:23, 3.60s/it] {'loss': 1.1871, 'grad_norm': 0.0004041077854453711, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:37<05:23, 3.60s/it] 83%|████████▎ | 431/520 [26:40<05:19, 3.59s/it] {'loss': 1.131, 'grad_norm': 0.0004172138934363122, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:40<05:19, 3.59s/it] 83%|████████▎ | 432/520 [26:44<05:16, 3.59s/it] {'loss': 1.0895, 'grad_norm': 0.0004647777319159949, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:44<05:16, 3.59s/it] 83%|████████▎ | 433/520 [26:47<05:12, 3.59s/it] {'loss': 1.2238, 'grad_norm': 0.00043760049841281537, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:47<05:12, 3.59s/it] 83%|████████▎ | 434/520 [26:51<05:09, 3.59s/it] {'loss': 0.9791, 'grad_norm': 0.00042131064972715824, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:51<05:09, 3.59s/it] 84%|████████▎ | 435/520 [26:55<05:04, 3.59s/it] {'loss': 1.2564, 'grad_norm': 0.0004782123356513106, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:55<05:04, 3.59s/it] 84%|████████▍ | 436/520 [26:58<05:00, 3.58s/it] {'loss': 1.0694, 'grad_norm': 0.0004336342836980504, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:58<05:00, 3.58s/it] 84%|████████▍ | 437/520 [27:02<04:56, 3.58s/it] {'loss': 1.2769, 'grad_norm': 0.0004167156922510038, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:02<04:56, 3.58s/it] 84%|████████▍ | 438/520 [27:05<04:53, 3.58s/it] {'loss': 1.0984, 'grad_norm': 0.0004240283881444762, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:05<04:53, 3.58s/it] 84%|████████▍ | 439/520 [27:09<04:49, 3.58s/it] {'loss': 1.1165, 'grad_norm': 0.0003259371678135388, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:09<04:49, 3.58s/it] 85%|████████▍ | 440/520 [27:13<04:46, 3.58s/it] {'loss': 1.135, 'grad_norm': 0.0004536397084036722, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:13<04:46, 3.58s/it] 85%|████████▍ | 441/520 [27:16<04:44, 3.60s/it] {'loss': 1.1255, 'grad_norm': 0.0004274673621691632, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:16<04:44, 3.60s/it] 85%|████████▌ | 442/520 [27:20<04:41, 3.60s/it] {'loss': 1.1978, 'grad_norm': 0.0004676823879242064, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:20<04:41, 3.60s/it] 85%|████████▌ | 443/520 [27:23<04:37, 3.60s/it] {'loss': 1.2031, 'grad_norm': 0.0004003892297991953, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:23<04:37, 3.60s/it] 85%|████████▌ | 444/520 [27:27<04:33, 3.60s/it] {'loss': 1.173, 'grad_norm': 0.0003683342066309572, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:27<04:33, 3.60s/it] 86%|████████▌ | 445/520 [27:31<04:30, 3.60s/it] {'loss': 1.1, 'grad_norm': 0.0003946193126094245, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:31<04:30, 3.60s/it] 86%|████████▌ | 446/520 [27:34<04:26, 3.60s/it] {'loss': 1.2047, 'grad_norm': 0.00036652987877548097, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:34<04:26, 3.60s/it] 86%|████████▌ | 447/520 [27:38<04:23, 3.60s/it] {'loss': 1.1672, 'grad_norm': 0.0004016907310140827, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:38<04:23, 3.60s/it] 86%|████████▌ | 448/520 [27:41<04:21, 3.63s/it] {'loss': 1.1699, 'grad_norm': 0.00046263177900048686, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:41<04:21, 3.63s/it] 86%|████████▋ | 449/520 [27:45<04:16, 3.62s/it] {'loss': 1.1658, 'grad_norm': 0.000397616461792323, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:45<04:16, 3.62s/it] 87%|████████▋ | 450/520 [27:49<04:12, 3.61s/it] {'loss': 1.1924, 'grad_norm': 0.0004170556405066807, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:49<04:12, 3.61s/it] 87%|████████▋ | 451/520 [27:52<04:10, 3.63s/it] {'loss': 1.196, 'grad_norm': 0.00042741895838623186, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:52<04:10, 3.63s/it] 87%|████████▋ | 452/520 [27:56<04:06, 3.63s/it] {'loss': 1.2127, 'grad_norm': 0.00037854502750740227, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:56<04:06, 3.63s/it] 87%|████████▋ | 453/520 [28:00<04:02, 3.63s/it] {'loss': 1.1893, 'grad_norm': 0.0003928239273266794, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:00<04:02, 3.63s/it] 87%|████████▋ | 454/520 [28:03<03:59, 3.63s/it] {'loss': 1.1052, 'grad_norm': 0.00041032489235984975, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:03<03:59, 3.63s/it] 88%|████████▊ | 455/520 [28:07<03:56, 3.63s/it] {'loss': 1.2432, 'grad_norm': 0.00041066839676938747, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:07<03:56, 3.63s/it] 88%|████████▊ | 456/520 [28:10<03:52, 3.63s/it] {'loss': 1.1813, 'grad_norm': 0.00042982048681180303, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:10<03:52, 3.63s/it] 88%|████████▊ | 457/520 [28:14<03:49, 3.64s/it] {'loss': 1.0666, 'grad_norm': 0.0003484445177074672, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:14<03:49, 3.64s/it] 88%|████████▊ | 458/520 [28:18<03:45, 3.63s/it] {'loss': 1.294, 'grad_norm': 0.0004674546668222541, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:18<03:45, 3.63s/it] 88%|████████▊ | 459/520 [28:21<03:41, 3.63s/it] {'loss': 1.2264, 'grad_norm': 0.00042420204247379807, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:21<03:41, 3.63s/it] 88%|████████▊ | 460/520 [28:25<03:38, 3.64s/it] {'loss': 1.1206, 'grad_norm': 0.00041599717198726057, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:25<03:38, 3.64s/it] 89%|████████▊ | 461/520 [28:29<03:34, 3.64s/it] {'loss': 1.1487, 'grad_norm': 0.00031934402861669153, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:29<03:34, 3.64s/it] 89%|████████▉ | 462/520 [28:32<03:31, 3.65s/it] {'loss': 1.2512, 'grad_norm': 0.0003853693703537895, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:32<03:31, 3.65s/it] 89%|████████▉ | 463/520 [28:36<03:27, 3.64s/it] {'loss': 1.0962, 'grad_norm': 0.00044331909922205073, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:36<03:27, 3.64s/it] 89%|████████▉ | 464/520 [28:40<03:23, 3.64s/it] {'loss': 1.2107, 'grad_norm': 0.0004181827730082276, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:40<03:23, 3.64s/it] 89%|████████▉ | 465/520 [28:43<03:19, 3.63s/it] {'loss': 1.3124, 'grad_norm': 0.00042198069582500023, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:43<03:19, 3.63s/it] 90%|████████▉ | 466/520 [28:47<03:17, 3.65s/it] {'loss': 1.2124, 'grad_norm': 0.00038544532806442854, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [28:47<03:17, 3.65s/it] 90%|████████▉ | 467/520 [28:51<03:13, 3.65s/it] {'loss': 1.1406, 'grad_norm': 0.000380292366918062, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [28:51<03:13, 3.65s/it] 90%|█████████ | 468/520 [28:54<03:09, 3.64s/it] {'loss': 1.1714, 'grad_norm': 0.0004652636377034552, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [28:54<03:09, 3.64s/it] 90%|█████████ | 469/520 [28:58<03:05, 3.63s/it] {'loss': 1.2446, 'grad_norm': 0.0005639743468416461, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [28:58<03:05, 3.63s/it] 90%|█████████ | 470/520 [29:01<03:01, 3.63s/it] {'loss': 1.1151, 'grad_norm': 0.0003825730730209243, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:01<03:01, 3.63s/it] 91%|█████████ | 471/520 [29:05<02:57, 3.63s/it] {'loss': 1.1472, 'grad_norm': 0.00044473523956009195, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:05<02:57, 3.63s/it] 91%|█████████ | 472/520 [29:09<02:54, 3.63s/it] {'loss': 1.1151, 'grad_norm': 0.0004373147560095306, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:09<02:54, 3.63s/it] 91%|█████████ | 473/520 [29:12<02:51, 3.64s/it] {'loss': 1.1885, 'grad_norm': 0.00044812459149505757, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:12<02:51, 3.64s/it] 91%|█████████ | 474/520 [29:16<02:48, 3.67s/it] {'loss': 1.1732, 'grad_norm': 0.0003844518898339143, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:16<02:48, 3.67s/it] 91%|█████████▏| 475/520 [29:20<02:47, 3.71s/it] {'loss': 1.0929, 'grad_norm': 0.00038294515346289575, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:20<02:47, 3.71s/it] 92%|█████████▏| 476/520 [29:24<02:44, 3.73s/it] {'loss': 1.1683, 'grad_norm': 0.0004295368905838451, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:24<02:44, 3.73s/it] 92%|█████████▏| 477/520 [29:28<02:41, 3.76s/it] {'loss': 1.1651, 'grad_norm': 0.00047777848543130213, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:28<02:41, 3.76s/it] 92%|█████████▏| 478/520 [29:31<02:38, 3.78s/it] {'loss': 1.1075, 'grad_norm': 0.0004035452031693407, 'learning_rate': 0.00340741737109318, 'epoch': 0.92} + 92%|█████████▏| 478/520 [29:31<02:38, 3.78s/it] 92%|█████████▏| 479/520 [29:35<02:35, 3.79s/it] {'loss': 1.1514, 'grad_norm': 0.0004776295004792494, 'learning_rate': 0.003247964765680389, 'epoch': 0.92} + 92%|█████████▏| 479/520 [29:35<02:35, 3.79s/it] 92%|█████████▏| 480/520 [29:39<02:29, 3.74s/it] {'loss': 1.1613, 'grad_norm': 0.00038154723308144085, 'learning_rate': 0.0030922713770922153, 'epoch': 0.92} + 92%|█████████▏| 480/520 [29:39<02:29, 3.74s/it] 92%|█████████▎| 481/520 [29:42<02:24, 3.71s/it] {'loss': 1.1526, 'grad_norm': 0.00040411101403657965, 'learning_rate': 0.0029403432546609046, 'epoch': 0.93} + 92%|█████████▎| 481/520 [29:42<02:24, 3.71s/it] 93%|█████████▎| 482/520 [29:46<02:20, 3.69s/it] {'loss': 1.1732, 'grad_norm': 0.00036773048615998795, 'learning_rate': 0.0027921863014225504, 'epoch': 0.93} + 93%|█████████▎| 482/520 [29:46<02:20, 3.69s/it] 93%|█████████▎| 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0.95} + 95%|█████████▍| 492/520 [30:23<01:43, 3.71s/it] 95%|█████████▍| 493/520 [30:27<01:39, 3.69s/it] {'loss': 1.1706, 'grad_norm': 0.0004277314603772508, 'learning_rate': 0.0014128981481764114, 'epoch': 0.95} + 95%|█████████▍| 493/520 [30:27<01:39, 3.69s/it] 95%|█████████▌| 494/520 [30:30<01:35, 3.68s/it] {'loss': 1.1845, 'grad_norm': 0.00037493154672626084, 'learning_rate': 0.0013104021143278911, 'epoch': 0.95} + 95%|█████████▌| 494/520 [30:30<01:35, 3.68s/it] 95%|█████████▌| 495/520 [30:34<01:31, 3.66s/it] {'loss': 1.1704, 'grad_norm': 0.0004291037577503598, 'learning_rate': 0.0012117405796285285, 'epoch': 0.95} + 95%|█████████▌| 495/520 [30:34<01:31, 3.66s/it] 95%|█████████▌| 496/520 [30:37<01:27, 3.66s/it] {'loss': 1.1023, 'grad_norm': 0.0004623148324161763, 'learning_rate': 0.0011169173774871477, 'epoch': 0.95} + 95%|█████████▌| 496/520 [30:37<01:27, 3.66s/it] 96%|█████████▌| 497/520 [30:41<01:24, 3.66s/it] {'loss': 1.104, 'grad_norm': 0.0003475788630071579, 'learning_rate': 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{'loss': 1.2746, 'grad_norm': 0.00036496768970770464, 'learning_rate': 0.0003281389042844918, 'epoch': 0.97} + 98%|█████████▊| 507/520 [31:18<00:48, 3.73s/it] 98%|█████████▊| 508/520 [31:22<00:45, 3.77s/it] {'loss': 1.2683, 'grad_norm': 0.000419961878889881, 'learning_rate': 0.00027962028188198705, 'epoch': 0.98} + 98%|█████████▊| 508/520 [31:22<00:45, 3.77s/it] 98%|█████████▊| 509/520 [31:26<00:41, 3.80s/it] {'loss': 1.2377, 'grad_norm': 0.0004218073364601494, 'learning_rate': 0.00023497620876711256, 'epoch': 0.98} + 98%|█████████▊| 509/520 [31:26<00:41, 3.80s/it] 98%|█████████▊| 510/520 [31:29<00:38, 3.80s/it] {'loss': 1.1879, 'grad_norm': 0.0004125171784447837, 'learning_rate': 0.00019420841954681523, 'epoch': 0.98} + 98%|█████████▊| 510/520 [31:29<00:38, 3.80s/it] 98%|█████████▊| 511/520 [31:33<00:33, 3.77s/it] {'loss': 1.1494, 'grad_norm': 0.0003981135886621586, 'learning_rate': 0.00015731849821833956, 'epoch': 0.98} + 98%|█████████▊| 511/520 [31:33<00:33, 3.77s/it] 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100%|██████████| 520/520 [32:07<00:00, 3.90s/it] 100%|██████████| 520/520 [32:07<00:00, 3.71s/it] +[2025-10-18 04:12:44,264] [INFO] [launch.py:348:main] Process 920997 exits successfully. +[2025-10-18 04:12:45,266] [INFO] [launch.py:348:main] Process 920999 exits successfully. +[2025-10-18 04:12:45,266] [INFO] [launch.py:348:main] Process 920996 exits successfully. +[2025-10-18 04:12:45,267] [INFO] [launch.py:348:main] Process 920998 exits successfully. +[2025-10-18 04:12:45,267] [INFO] [launch.py:348:main] Process 921000 exits successfully. +[2025-10-18 04:12:45,267] [INFO] [launch.py:348:main] Process 921002 exits successfully. +[2025-10-18 04:12:46,269] [INFO] [launch.py:348:main] Process 921001 exits successfully. +[2025-10-18 04:12:49,273] [INFO] [launch.py:348:main] Process 920995 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.1_2e-1_connector-9.0_2.1_2e-1_ablation_20251018_033900.log +Timestamp: 2025-10-18 04:12:51 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation_20251018_041251.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation_20251018_041251.log new file mode 100644 index 0000000000000000000000000000000000000000..3f4fabf4ea685d7ca9656f5edb9a0ed4972b1f5a --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation_20251018_041251.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation_20251018_041251.log +Timestamp: 2025-10-18 04:12:51 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:12:54,363] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:12:57,071] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 04:12:57,072] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 2.3 --temperature_mlp_text 2.3 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 2.3 --temperature_mlp_vision 2.3 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 2.3 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:12:59,683] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:00,735] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 04:13:00,735] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 04:13:00,735] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 04:13:00,735] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 04:13:00,735] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 04:13:00,735] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 04:13:00,735] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 04:13:00,738] [INFO] [launch.py:253:main] process 942864 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,739] [INFO] [launch.py:253:main] process 942865 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', 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'--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,741] [INFO] [launch.py:253:main] process 942866 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', 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'--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,743] [INFO] [launch.py:253:main] process 942867 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', 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'--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,745] [INFO] [launch.py:253:main] process 942868 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', 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'--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', 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'--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,749] [INFO] [launch.py:253:main] process 942870 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:13:00,751] [INFO] [launch.py:253:main] process 942871 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.3', '--temperature_mlp_text', '2.3', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.3', '--temperature_mlp_vision', '2.3', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.3', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:13:06,683] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,088] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:07,533] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,757] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,861] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,870] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,873] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,907] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,907] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:13:07,940] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,181] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,291] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,297] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,300] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,334] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:13:08,334] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 04:13:08,335] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.3, 'temperature_mlp': 2.3, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.3, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.3, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.3, + "temperature_mlp": 2.3, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:942864:942864 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:942871:942871 [7] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:942866:942866 [2] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:942869:942869 [5] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Using network Socket +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Using network Socket 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INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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+ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942865:944529 [1] NCCL INFO ncclCommInitRank comm 0x55f882b85960 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942866:944525 [2] NCCL INFO ncclCommInitRank comm 0x55ea21112730 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942864:944523 [0] NCCL INFO ncclCommInitRank comm 0x5599babb8280 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942870:944528 [6] NCCL INFO ncclCommInitRank comm 0x55be4ec46960 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942869:944526 [5] NCCL INFO ncclCommInitRank comm 0x5581f0b9abd0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942868:944530 [4] NCCL INFO ncclCommInitRank comm 0x55de57294020 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942867:944527 [3] NCCL INFO ncclCommInitRank comm 0x56084d2539a0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x7e131f6439e56b2a - Init COMPLETE +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:942871:944524 [7] NCCL INFO ncclCommInitRank comm 0x55c030825740 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x7e131f6439e56b2a - Init COMPLETE +[2025-10-18 04:13:52,141] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 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'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 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'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 04:13:53,896] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 04:14:12,110 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 04:14:12,118 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:005->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942864:949546 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942869:949547 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942865:949549 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942866:949550 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942868:949551 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942871:949553 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942870:949548 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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0x368f18d9f2744fdf - Init COMPLETE +ywang29-vrdb-test1-worker-0:942867:949552 [3] NCCL INFO ncclCommInitRank comm 0x7f5d8806abf0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x368f18d9f2744fdf - Init COMPLETE + 0%| | 1/520 [00:14<2:08:14, 14.83s/it] {'loss': 2.045, 'grad_norm': 0.005073159929617847, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:08:14, 14.83s/it] 0%| | 2/520 [00:18<1:11:28, 8.28s/it] {'loss': 2.0521, 'grad_norm': 0.0055000818485863206, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:11:28, 8.28s/it] 1%| | 3/520 [00:22<53:41, 6.23s/it] {'loss': 2.189, 'grad_norm': 0.006321505243793667, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:22<53:41, 6.23s/it] 1%| | 4/520 [00:25<44:46, 5.21s/it] {'loss': 2.0657, 'grad_norm': 0.005257828563996339, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<44:46, 5.21s/it] 1%| | 5/520 [00:29<39:52, 4.64s/it] {'loss': 2.2278, 'grad_norm': 0.005767428287973715, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<39:52, 4.64s/it] 1%| | 6/520 [00:33<36:57, 4.31s/it] {'loss': 1.6771, 'grad_norm': 0.0029532985449857024, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<36:57, 4.31s/it] 1%|▏ | 7/520 [00:36<34:57, 4.09s/it] {'loss': 1.8551, 'grad_norm': 0.0038286392285163803, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<34:57, 4.09s/it] 2%|▏ | 8/520 [00:41<35:36, 4.17s/it] {'loss': 1.6887, 'grad_norm': 0.002364367971289622, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:41<35:36, 4.17s/it] 2%|▏ | 9/520 [00:45<34:42, 4.07s/it] {'loss': 1.6796, 'grad_norm': 0.0012182642558740812, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:45<34:42, 4.07s/it] 2%|▏ | 10/520 [00:49<34:11, 4.02s/it] {'loss': 1.5128, 'grad_norm': 0.0010926429476120283, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:49<34:11, 4.02s/it] 2%|▏ | 11/520 [00:53<34:02, 4.01s/it] {'loss': 1.5233, 'grad_norm': 0.0008147154531647395, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:53<34:02, 4.01s/it] 2%|▏ | 12/520 [00:56<33:29, 3.96s/it] {'loss': 1.3807, 'grad_norm': 0.0006174938154405346, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:56<33:29, 3.96s/it][2025-10-18 04:15:17,683] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:01<34:19, 4.06s/it] {'loss': 1.4394, 'grad_norm': 0.0006066971195589395, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:01<34:19, 4.06s/it] 3%|▎ | 14/520 [01:04<33:13, 3.94s/it] {'loss': 1.4802, 'grad_norm': 0.0005733926685929531, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:04<33:13, 3.94s/it] 3%|▎ | 15/520 [01:08<32:20, 3.84s/it] {'loss': 1.4005, 'grad_norm': 0.0004144413991543411, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:08<32:20, 3.84s/it] 3%|▎ | 16/520 [01:12<31:38, 3.77s/it] {'loss': 1.3786, 'grad_norm': 0.0005215551013764742, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:12<31:38, 3.77s/it] 3%|▎ | 17/520 [01:15<31:10, 3.72s/it] {'loss': 1.5059, 'grad_norm': 0.00054720532163621, 'learning_rate': 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'learning_rate': 0.08509577338238256, 'epoch': 0.56} + 56%|█████▌ | 292/520 [18:10<13:45, 3.62s/it] 56%|█████▋ | 293/520 [18:13<13:41, 3.62s/it] {'loss': 1.1625, 'grad_norm': 0.0004820450692730565, 'learning_rate': 0.08447969714556484, 'epoch': 0.56} + 56%|█████▋ | 293/520 [18:13<13:41, 3.62s/it] 57%|█████▋ | 294/520 [18:17<13:52, 3.68s/it] {'loss': 1.1804, 'grad_norm': 0.00048622773581919414, 'learning_rate': 0.08386422393671933, 'epoch': 0.57} + 57%|█████▋ | 294/520 [18:17<13:52, 3.68s/it] 57%|█████▋ | 295/520 [18:21<13:58, 3.73s/it] {'loss': 1.162, 'grad_norm': 0.00041794957213561594, 'learning_rate': 0.08324937766952638, 'epoch': 0.57} + 57%|█████▋ | 295/520 [18:21<13:58, 3.73s/it] 57%|█████▋ | 296/520 [18:25<14:02, 3.76s/it] {'loss': 1.1308, 'grad_norm': 0.0004835508293544831, 'learning_rate': 0.08263518223330697, 'epoch': 0.57} + 57%|█████▋ | 296/520 [18:25<14:02, 3.76s/it] 57%|█████▋ | 297/520 [18:29<14:03, 3.78s/it] {'loss': 1.2609, 'grad_norm': 0.0005029581940637687, 'learning_rate': 0.08202166149209474, 'epoch': 0.57} + 57%|█████▋ | 297/520 [18:29<14:03, 3.78s/it] 57%|█████▋ | 298/520 [18:32<14:03, 3.80s/it] {'loss': 1.2213, 'grad_norm': 0.0004355186487272584, 'learning_rate': 0.08140883928370855, 'epoch': 0.57} + 57%|█████▋ | 298/520 [18:32<14:03, 3.80s/it] 57%|█████▊ | 299/520 [18:36<14:02, 3.81s/it] {'loss': 1.2181, 'grad_norm': 0.00043377353378637484, 'learning_rate': 0.0807967394188264, 'epoch': 0.57} + 57%|█████▊ | 299/520 [18:36<14:02, 3.81s/it] 58%|█████▊ | 300/520 [18:40<13:57, 3.81s/it] {'loss': 1.2694, 'grad_norm': 0.0004656746694900133, 'learning_rate': 0.08018538568006027, 'epoch': 0.58} + 58%|█████▊ | 300/520 [18:40<13:57, 3.81s/it] 58%|█████▊ | 301/520 [18:44<13:55, 3.82s/it] {'loss': 1.2615, 'grad_norm': 0.0004734586018016699, 'learning_rate': 0.07957480182103199, 'epoch': 0.58} + 58%|█████▊ | 301/520 [18:44<13:55, 3.82s/it] 58%|█████▊ | 302/520 [18:48<13:54, 3.83s/it] {'loss': 1.2166, 'grad_norm': 0.0004564385049353001, 'learning_rate': 0.07896501156545044, 'epoch': 0.58} + 58%|█████▊ | 302/520 [18:48<13:54, 3.83s/it] 58%|█████▊ | 303/520 [18:52<13:51, 3.83s/it] {'loss': 1.1787, 'grad_norm': 0.000552287441063209, 'learning_rate': 0.07835603860618973, 'epoch': 0.58} + 58%|█████▊ | 303/520 [18:52<13:51, 3.83s/it] 58%|█████▊ | 304/520 [18:56<14:09, 3.93s/it] {'loss': 1.1319, 'grad_norm': 0.0005238218031284752, 'learning_rate': 0.07774790660436857, 'epoch': 0.58} + 58%|█████▊ | 304/520 [18:56<14:09, 3.93s/it] 59%|█████▊ | 305/520 [19:00<13:58, 3.90s/it] {'loss': 1.2747, 'grad_norm': 0.0005515365298639141, 'learning_rate': 0.07714063918843106, 'epoch': 0.59} + 59%|█████▊ | 305/520 [19:00<13:58, 3.90s/it] 59%|█████▉ | 306/520 [19:03<13:48, 3.87s/it] {'loss': 1.2257, 'grad_norm': 0.0004885936331879188, 'learning_rate': 0.0765342599532285, 'epoch': 0.59} + 59%|█████▉ | 306/520 [19:03<13:48, 3.87s/it] 59%|█████▉ | 307/520 [19:07<13:43, 3.87s/it] {'loss': 1.1712, 'grad_norm': 0.00045490680496947436, 'learning_rate': 0.07592879245910272, 'epoch': 0.59} + 59%|█████▉ | 307/520 [19:07<13:43, 3.87s/it] 59%|█████▉ | 308/520 [19:11<13:36, 3.85s/it] {'loss': 1.2835, 'grad_norm': 0.0005451628936520057, 'learning_rate': 0.07532426023097064, 'epoch': 0.59} + 59%|█████▉ | 308/520 [19:11<13:36, 3.85s/it] 59%|█████▉ | 309/520 [19:15<13:29, 3.84s/it] {'loss': 1.1776, 'grad_norm': 0.0004749540115251483, 'learning_rate': 0.07472068675741024, 'epoch': 0.59} + 59%|█████▉ | 309/520 [19:15<13:29, 3.84s/it] 60%|█████▉ | 310/520 [19:19<13:30, 3.86s/it] {'loss': 1.1525, 'grad_norm': 0.0004690501685010774, 'learning_rate': 0.07411809548974792, 'epoch': 0.6} + 60%|█████▉ | 310/520 [19:19<13:30, 3.86s/it] 60%|█████▉ | 311/520 [19:23<13:24, 3.85s/it] {'loss': 1.1409, 'grad_norm': 0.0004907118432775712, 'learning_rate': 0.07351650984114727, 'epoch': 0.6} + 60%|█████▉ | 311/520 [19:23<13:24, 3.85s/it] 60%|██████ | 312/520 [19:26<13:19, 3.85s/it] {'loss': 1.1286, 'grad_norm': 0.0004809085796935301, 'learning_rate': 0.0729159531856995, 'epoch': 0.6} + 60%|██████ | 312/520 [19:26<13:19, 3.85s/it] 60%|██████ | 313/520 [19:30<13:15, 3.84s/it] {'loss': 1.104, 'grad_norm': 0.0004604844866186743, 'learning_rate': 0.07231644885751508, 'epoch': 0.6} + 60%|██████ | 313/520 [19:30<13:15, 3.84s/it] 60%|██████ | 314/520 [19:35<13:36, 3.97s/it] {'loss': 1.1473, 'grad_norm': 0.000457325320003833, 'learning_rate': 0.07171802014981725, 'epoch': 0.6} + 60%|██████ | 314/520 [19:35<13:36, 3.97s/it] 61%|██████ | 315/520 [19:38<13:23, 3.92s/it] {'loss': 1.1809, 'grad_norm': 0.0005610885599408175, 'learning_rate': 0.07112069031403703, 'epoch': 0.61} + 61%|██████ | 315/520 [19:38<13:23, 3.92s/it] 61%|██████ | 316/520 [19:43<13:37, 4.01s/it] {'loss': 1.1345, 'grad_norm': 0.0004996090792450656, 'learning_rate': 0.07052448255890957, 'epoch': 0.61} + 61%|██████ | 316/520 [19:43<13:37, 4.01s/it] 61%|██████ | 317/520 [19:46<13:22, 3.95s/it] {'loss': 1.1316, 'grad_norm': 0.00041108932392113645, 'learning_rate': 0.0699294200495727, 'epoch': 0.61} + 61%|██████ | 317/520 [19:46<13:22, 3.95s/it] 61%|██████ | 318/520 [19:50<13:09, 3.91s/it] {'loss': 1.243, 'grad_norm': 0.000489432639229685, 'learning_rate': 0.06933552590666658, 'epoch': 0.61} + 61%|██████ | 318/520 [19:50<13:09, 3.91s/it] 61%|██████▏ | 319/520 [19:54<13:19, 3.98s/it] {'loss': 1.1281, 'grad_norm': 0.0004157562264633972, 'learning_rate': 0.06874282320543557, 'epoch': 0.61} + 61%|██████▏ | 319/520 [19:54<13:19, 3.98s/it] 62%|██████▏ | 320/520 [19:58<13:06, 3.93s/it] {'loss': 1.0669, 'grad_norm': 0.0004971492980210942, 'learning_rate': 0.06815133497483157, 'epoch': 0.62} + 62%|██████▏ | 320/520 [19:58<13:06, 3.93s/it] 62%|██████▏ | 321/520 [20:02<12:56, 3.90s/it] {'loss': 1.2649, 'grad_norm': 0.0004993126126639397, 'learning_rate': 0.06756108419661931, 'epoch': 0.62} + 62%|██████▏ | 321/520 [20:02<12:56, 3.90s/it] 62%|██████▏ | 322/520 [20:06<12:48, 3.88s/it] {'loss': 1.0819, 'grad_norm': 0.00045306824791475334, 'learning_rate': 0.06697209380448332, 'epoch': 0.62} + 62%|██████▏ | 322/520 [20:06<12:48, 3.88s/it] 62%|██████▏ | 323/520 [20:10<12:40, 3.86s/it] {'loss': 1.1562, 'grad_norm': 0.0004608859795249279, 'learning_rate': 0.06638438668313694, 'epoch': 0.62} + 62%|██████▏ | 323/520 [20:10<12:40, 3.86s/it] 62%|██████▏ | 324/520 [20:13<12:34, 3.85s/it] {'loss': 1.2118, 'grad_norm': 0.000466247128750601, 'learning_rate': 0.06579798566743314, 'epoch': 0.62} + 62%|██████▏ | 324/520 [20:13<12:34, 3.85s/it] 62%|██████▎ | 325/520 [20:17<12:30, 3.85s/it] {'loss': 1.2055, 'grad_norm': 0.0004946166022023645, 'learning_rate': 0.06521291354147728, 'epoch': 0.62} + 62%|██████▎ | 325/520 [20:17<12:30, 3.85s/it] 63%|██████▎ | 326/520 [20:21<12:25, 3.84s/it] {'loss': 1.2086, 'grad_norm': 0.0004982745281985857, 'learning_rate': 0.06462919303774187, 'epoch': 0.63} + 63%|██████▎ | 326/520 [20:21<12:25, 3.84s/it] 63%|██████▎ | 327/520 [20:25<12:19, 3.83s/it] {'loss': 1.1811, 'grad_norm': 0.0004836472953307938, 'learning_rate': 0.06404684683618325, 'epoch': 0.63} + 63%|██████▎ | 327/520 [20:25<12:19, 3.83s/it] 63%|██████▎ | 328/520 [20:29<12:14, 3.83s/it] {'loss': 1.2451, 'grad_norm': 0.0004872516162930565, 'learning_rate': 0.0634658975633605, 'epoch': 0.63} + 63%|██████▎ | 328/520 [20:29<12:14, 3.83s/it] 63%|██████▎ | 329/520 [20:33<12:11, 3.83s/it] {'loss': 1.1301, 'grad_norm': 0.00040651729636766057, 'learning_rate': 0.06288636779155621, 'epoch': 0.63} + 63%|██████▎ | 329/520 [20:33<12:11, 3.83s/it] 63%|██████▎ | 330/520 [20:36<12:08, 3.83s/it] {'loss': 1.2057, 'grad_norm': 0.0004392049072579156, 'learning_rate': 0.06230828003789948, 'epoch': 0.63} + 63%|██████▎ | 330/520 [20:36<12:08, 3.83s/it] 64%|██████▎ | 331/520 [20:40<12:03, 3.83s/it] {'loss': 1.1639, 'grad_norm': 0.0004709885373369861, 'learning_rate': 0.06173165676349103, 'epoch': 0.64} + 64%|██████▎ | 331/520 [20:40<12:03, 3.83s/it] 64%|██████▍ | 332/520 [20:44<11:59, 3.83s/it] {'loss': 1.2114, 'grad_norm': 0.000414202355863996, 'learning_rate': 0.06115652037253053, 'epoch': 0.64} + 64%|██████▍ | 332/520 [20:44<11:59, 3.83s/it] 64%|██████▍ | 333/520 [20:48<11:55, 3.83s/it] {'loss': 1.298, 'grad_norm': 0.0004893465398459883, 'learning_rate': 0.06058289321144608, 'epoch': 0.64} + 64%|██████▍ | 333/520 [20:48<11:55, 3.83s/it] 64%|██████▍ | 334/520 [20:52<11:50, 3.82s/it] {'loss': 1.2109, 'grad_norm': 0.0005022355721628709, 'learning_rate': 0.06001079756802592, 'epoch': 0.64} + 64%|██████▍ | 334/520 [20:52<11:50, 3.82s/it] 64%|██████▍ | 335/520 [20:56<11:46, 3.82s/it] {'loss': 1.2114, 'grad_norm': 0.0004701511928629364, 'learning_rate': 0.059440255670552514, 'epoch': 0.64} + 64%|██████▍ | 335/520 [20:56<11:46, 3.82s/it] 65%|██████▍ | 336/520 [20:59<11:44, 3.83s/it] {'loss': 1.1222, 'grad_norm': 0.0005140360140345993, 'learning_rate': 0.05887128968693887, 'epoch': 0.65} + 65%|██████▍ | 336/520 [20:59<11:44, 3.83s/it] 65%|██████▍ | 337/520 [21:03<11:41, 3.83s/it] {'loss': 1.1131, 'grad_norm': 0.0004692434308529883, 'learning_rate': 0.058303921723867225, 'epoch': 0.65} + 65%|██████▍ | 337/520 [21:03<11:41, 3.83s/it] 65%|██████▌ | 338/520 [21:07<11:35, 3.82s/it] {'loss': 1.2156, 'grad_norm': 0.0004548403998961154, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:07<11:35, 3.82s/it] 65%|██████▌ | 339/520 [21:11<11:31, 3.82s/it] {'loss': 1.1583, 'grad_norm': 0.0005062619421828143, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:11<11:31, 3.82s/it] 65%|██████▌ | 340/520 [21:15<11:27, 3.82s/it] {'loss': 1.1468, 'grad_norm': 0.00045989400237461155, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:15<11:27, 3.82s/it] 66%|██████▌ | 341/520 [21:18<11:18, 3.79s/it] {'loss': 1.1782, 'grad_norm': 0.0005116476814932497, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:18<11:18, 3.79s/it] 66%|██████▌ | 342/520 [21:22<11:07, 3.75s/it] {'loss': 1.1911, 'grad_norm': 0.0005307912028330985, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:22<11:07, 3.75s/it] 66%|██████▌ | 343/520 [21:26<10:58, 3.72s/it] {'loss': 1.1376, 'grad_norm': 0.0004636379261433932, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:26<10:58, 3.72s/it] 66%|██████▌ | 344/520 [21:29<10:50, 3.70s/it] {'loss': 1.1361, 'grad_norm': 0.00043296805790241907, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:29<10:50, 3.70s/it] 66%|██████▋ | 345/520 [21:33<10:43, 3.68s/it] {'loss': 1.2329, 'grad_norm': 0.0004699837641898624, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:33<10:43, 3.68s/it] 67%|██████▋ | 346/520 [21:37<10:37, 3.66s/it] {'loss': 1.1608, 'grad_norm': 0.0005322952931599368, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:37<10:37, 3.66s/it] 67%|██████▋ | 347/520 [21:40<10:32, 3.66s/it] {'loss': 1.1541, 'grad_norm': 0.0004238389172999249, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:40<10:32, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:44<10:27, 3.65s/it] {'loss': 1.109, 'grad_norm': 0.0005663687956684555, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:44<10:27, 3.65s/it] 67%|██████▋ | 349/520 [21:47<10:22, 3.64s/it] {'loss': 1.1439, 'grad_norm': 0.0004650208153076421, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:47<10:22, 3.64s/it] 67%|██████▋ | 350/520 [21:51<10:17, 3.63s/it] {'loss': 1.1898, 'grad_norm': 0.0005824934450065464, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:51<10:17, 3.63s/it] 68%|██████▊ | 351/520 [21:55<10:15, 3.64s/it] {'loss': 1.102, 'grad_norm': 0.00043883898125829856, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:55<10:15, 3.64s/it] 68%|██████▊ | 352/520 [21:58<10:11, 3.64s/it] {'loss': 1.2132, 'grad_norm': 0.0004364511050640117, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:58<10:11, 3.64s/it] 68%|██████▊ | 353/520 [22:02<10:09, 3.65s/it] {'loss': 1.1347, 'grad_norm': 0.00041474774107053223, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:02<10:09, 3.65s/it] 68%|██████▊ | 354/520 [22:06<10:03, 3.63s/it] {'loss': 1.223, 'grad_norm': 0.0004396130363768432, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:06<10:03, 3.63s/it] 68%|██████▊ | 355/520 [22:09<09:59, 3.64s/it] {'loss': 1.1641, 'grad_norm': 0.00047525149483237575, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:09<09:59, 3.64s/it] 68%|██████▊ | 356/520 [22:13<09:56, 3.64s/it] {'loss': 1.1622, 'grad_norm': 0.00047474998502503013, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:13<09:56, 3.64s/it] 69%|██████▊ | 357/520 [22:17<09:59, 3.68s/it] {'loss': 1.2008, 'grad_norm': 0.0004398092897398413, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:17<09:59, 3.68s/it] 69%|██████▉ | 358/520 [22:21<10:01, 3.72s/it] {'loss': 1.1269, 'grad_norm': 0.0004584624055036559, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:21<10:01, 3.72s/it] 69%|██████▉ | 359/520 [22:24<10:03, 3.75s/it] {'loss': 1.1674, 'grad_norm': 0.00048157204075384625, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:24<10:03, 3.75s/it] 69%|██████▉ | 360/520 [22:28<10:01, 3.76s/it] {'loss': 1.1736, 'grad_norm': 0.0005491523460206181, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:28<10:01, 3.76s/it] 69%|██████▉ | 361/520 [22:32<09:59, 3.77s/it] {'loss': 1.1939, 'grad_norm': 0.00039717298592002514, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:32<09:59, 3.77s/it] 70%|██████▉ | 362/520 [22:36<09:57, 3.78s/it] {'loss': 1.1724, 'grad_norm': 0.0005223308532593574, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:36<09:57, 3.78s/it] 70%|██████▉ | 363/520 [22:40<09:55, 3.79s/it] {'loss': 1.2036, 'grad_norm': 0.0004533758534502896, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:40<09:55, 3.79s/it] 70%|███████ | 364/520 [22:43<09:53, 3.81s/it] {'loss': 1.2049, 'grad_norm': 0.0004526363258111215, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:43<09:53, 3.81s/it] 70%|███████ | 365/520 [22:47<09:48, 3.79s/it] {'loss': 1.2533, 'grad_norm': 0.00047348673939091065, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:47<09:48, 3.79s/it] 70%|███████ | 366/520 [22:51<09:36, 3.74s/it] {'loss': 1.2221, 'grad_norm': 0.0004331172583870929, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:51<09:36, 3.74s/it] 71%|███████ | 367/520 [22:54<09:28, 3.72s/it] {'loss': 1.2212, 'grad_norm': 0.0004796438642309064, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:54<09:28, 3.72s/it] 71%|███████ | 368/520 [22:58<09:20, 3.69s/it] {'loss': 1.0746, 'grad_norm': 0.00047180858642112666, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:58<09:20, 3.69s/it] 71%|███████ | 369/520 [23:02<09:13, 3.67s/it] {'loss': 1.1692, 'grad_norm': 0.0004288206658736541, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:02<09:13, 3.67s/it] 71%|███████ | 370/520 [23:05<09:06, 3.64s/it] {'loss': 1.1346, 'grad_norm': 0.0004295671521703597, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:05<09:06, 3.64s/it] 71%|███████▏ | 371/520 [23:09<09:00, 3.63s/it] {'loss': 1.1243, 'grad_norm': 0.0004979606851507603, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:09<09:00, 3.63s/it] 72%|███████▏ | 372/520 [23:12<08:56, 3.63s/it] {'loss': 1.2327, 'grad_norm': 0.0004905616017641972, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:12<08:56, 3.63s/it] 72%|███████▏ | 373/520 [23:16<08:52, 3.62s/it] {'loss': 1.1241, 'grad_norm': 0.0004721530687952921, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:16<08:52, 3.62s/it] 72%|███████▏ | 374/520 [23:20<08:47, 3.62s/it] {'loss': 1.2205, 'grad_norm': 0.000509370390641211, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:20<08:47, 3.62s/it] 72%|███████▏ | 375/520 [23:23<08:43, 3.61s/it] {'loss': 1.1348, 'grad_norm': 0.00046482078359793595, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:23<08:43, 3.61s/it] 72%|███████▏ | 376/520 [23:27<08:42, 3.63s/it] {'loss': 1.2425, 'grad_norm': 0.0004440462082260945, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:27<08:42, 3.63s/it] 72%|███████▎ | 377/520 [23:31<08:38, 3.62s/it] {'loss': 1.1714, 'grad_norm': 0.0004881566955101341, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:31<08:38, 3.62s/it] 73%|███████▎ | 378/520 [23:34<08:34, 3.62s/it] {'loss': 1.2358, 'grad_norm': 0.00044157089924798984, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:34<08:34, 3.62s/it] 73%|███████▎ | 379/520 [23:38<08:30, 3.62s/it] {'loss': 1.2005, 'grad_norm': 0.0004431008980021601, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:38<08:30, 3.62s/it] 73%|███████▎ | 380/520 [23:41<08:26, 3.61s/it] {'loss': 1.2122, 'grad_norm': 0.00045156974573379636, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:41<08:26, 3.61s/it] 73%|███████▎ | 381/520 [23:45<08:23, 3.62s/it] {'loss': 1.2127, 'grad_norm': 0.00044349174168686726, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:45<08:23, 3.62s/it] 73%|███████▎ | 382/520 [23:49<08:22, 3.64s/it] {'loss': 1.1865, 'grad_norm': 0.0004286299971382413, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:49<08:22, 3.64s/it] 74%|███████▎ | 383/520 [23:52<08:18, 3.64s/it] {'loss': 1.056, 'grad_norm': 0.0005339132736477126, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:52<08:18, 3.64s/it] 74%|███████▍ | 384/520 [23:56<08:14, 3.64s/it] {'loss': 1.2025, 'grad_norm': 0.00043407984696172534, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:56<08:14, 3.64s/it] 74%|███████▍ | 385/520 [24:00<08:10, 3.64s/it] {'loss': 1.1993, 'grad_norm': 0.0004385678188364268, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:00<08:10, 3.64s/it] 74%|███████▍ | 386/520 [24:03<08:06, 3.63s/it] {'loss': 1.1498, 'grad_norm': 0.0004114280300728506, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:03<08:06, 3.63s/it] 74%|███████▍ | 387/520 [24:07<08:03, 3.63s/it] {'loss': 1.2301, 'grad_norm': 0.0004407865310449192, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:07<08:03, 3.63s/it] 75%|███████▍ | 388/520 [24:10<07:58, 3.63s/it] {'loss': 1.1109, 'grad_norm': 0.0004509046467611904, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:10<07:58, 3.63s/it] 75%|███████▍ | 389/520 [24:14<08:02, 3.68s/it] {'loss': 1.1569, 'grad_norm': 0.0006978762209509851, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:14<08:02, 3.68s/it] 75%|███████▌ | 390/520 [24:18<08:06, 3.74s/it] {'loss': 1.2243, 'grad_norm': 0.0004426884592664786, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:18<08:06, 3.74s/it] 75%|███████▌ | 391/520 [24:22<08:08, 3.79s/it] {'loss': 1.2817, 'grad_norm': 0.0004896041564569886, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:22<08:08, 3.79s/it] 75%|███████▌ | 392/520 [24:26<08:06, 3.80s/it] {'loss': 1.1109, 'grad_norm': 0.00046239708653267, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:26<08:06, 3.80s/it] 76%|███████▌ | 393/520 [24:30<08:06, 3.83s/it] {'loss': 1.0962, 'grad_norm': 0.00037775371740229836, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:30<08:06, 3.83s/it] 76%|███████▌ | 394/520 [24:34<08:02, 3.83s/it] {'loss': 1.182, 'grad_norm': 0.0004888255688149702, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:34<08:02, 3.83s/it] 76%|███████▌ | 395/520 [24:37<07:55, 3.81s/it] {'loss': 1.1475, 'grad_norm': 0.0004909983850733665, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:37<07:55, 3.81s/it] 76%|███████▌ | 396/520 [24:41<07:43, 3.74s/it] {'loss': 1.2222, 'grad_norm': 0.0004996214393564238, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:41<07:43, 3.74s/it] 76%|███████▋ | 397/520 [24:45<07:36, 3.71s/it] {'loss': 1.1976, 'grad_norm': 0.0004396646992029418, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:45<07:36, 3.71s/it] 77%|███████▋ | 398/520 [24:48<07:30, 3.69s/it] {'loss': 1.1916, 'grad_norm': 0.00048768817491903657, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:48<07:30, 3.69s/it] 77%|███████▋ | 399/520 [24:52<07:25, 3.68s/it] {'loss': 1.1262, 'grad_norm': 0.00045200334396476317, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:52<07:25, 3.68s/it] 77%|███████▋ | 400/520 [24:56<07:22, 3.68s/it] {'loss': 1.1596, 'grad_norm': 0.0004259690057190515, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:56<07:22, 3.68s/it] 77%|███████▋ | 401/520 [24:59<07:19, 3.69s/it] {'loss': 1.0337, 'grad_norm': 0.0005251193904223851, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:59<07:19, 3.69s/it] 77%|███████▋ | 402/520 [25:03<07:15, 3.69s/it] {'loss': 1.1637, 'grad_norm': 0.00047897659476388966, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:03<07:15, 3.69s/it] 78%|███████▊ | 403/520 [25:07<07:10, 3.68s/it] {'loss': 1.1849, 'grad_norm': 0.0005045855195247573, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:07<07:10, 3.68s/it] 78%|███████▊ | 404/520 [25:11<07:12, 3.73s/it] {'loss': 1.0973, 'grad_norm': 0.0005337799554362024, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:11<07:12, 3.73s/it] 78%|███████▊ | 405/520 [25:14<07:15, 3.79s/it] {'loss': 1.1411, 'grad_norm': 0.00043539081316669773, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:14<07:15, 3.79s/it] 78%|███████▊ | 406/520 [25:18<07:15, 3.82s/it] {'loss': 1.0668, 'grad_norm': 0.0006181382452772908, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:18<07:15, 3.82s/it] 78%|███████▊ | 407/520 [25:22<07:13, 3.84s/it] {'loss': 1.2602, 'grad_norm': 0.0004892483600423067, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:22<07:13, 3.84s/it] 78%|███████▊ | 408/520 [25:26<07:03, 3.78s/it] {'loss': 1.1787, 'grad_norm': 0.0005661871617105734, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:26<07:03, 3.78s/it] 79%|███████▊ | 409/520 [25:29<06:54, 3.73s/it] {'loss': 1.2918, 'grad_norm': 0.0005353316440864036, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:29<06:54, 3.73s/it] 79%|███████▉ | 410/520 [25:33<06:48, 3.71s/it] {'loss': 1.0365, 'grad_norm': 0.00047727022161834007, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:33<06:48, 3.71s/it] 79%|███████▉ | 411/520 [25:37<06:42, 3.69s/it] {'loss': 1.2735, 'grad_norm': 0.0004957345144464854, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:37<06:42, 3.69s/it] 79%|███████▉ | 412/520 [25:40<06:36, 3.68s/it] {'loss': 1.1841, 'grad_norm': 0.0004488685680054989, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:40<06:36, 3.68s/it] 79%|███████▉ | 413/520 [25:44<06:32, 3.67s/it] {'loss': 1.1577, 'grad_norm': 0.00046051711015381383, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:44<06:32, 3.67s/it] 80%|███████▉ | 414/520 [25:48<06:28, 3.67s/it] {'loss': 0.9703, 'grad_norm': 0.00039172012582664513, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:48<06:28, 3.67s/it] 80%|███████▉ | 415/520 [25:51<06:24, 3.67s/it] {'loss': 1.1662, 'grad_norm': 0.0004507183617517377, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:51<06:24, 3.67s/it] 80%|████████ | 416/520 [25:55<06:19, 3.65s/it] {'loss': 1.0693, 'grad_norm': 0.0005443896650813024, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:55<06:19, 3.65s/it] 80%|████████ | 417/520 [25:59<06:16, 3.66s/it] {'loss': 1.231, 'grad_norm': 0.0004555235691868739, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:59<06:16, 3.66s/it] 80%|████████ | 418/520 [26:02<06:12, 3.65s/it] {'loss': 1.2261, 'grad_norm': 0.0004556989181696274, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:02<06:12, 3.65s/it] 81%|████████ | 419/520 [26:06<06:07, 3.64s/it] {'loss': 1.2212, 'grad_norm': 0.000499664554324811, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:06<06:07, 3.64s/it] 81%|████████ | 420/520 [26:10<06:03, 3.63s/it] {'loss': 1.111, 'grad_norm': 0.0004708418823635556, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:10<06:03, 3.63s/it] 81%|████████ | 421/520 [26:13<05:59, 3.63s/it] {'loss': 1.0512, 'grad_norm': 0.0004969724519303007, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:13<05:59, 3.63s/it] 81%|████████ | 422/520 [26:17<05:56, 3.63s/it] {'loss': 1.1721, 'grad_norm': 0.0004886994700210304, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:17<05:56, 3.63s/it] 81%|████████▏ | 423/520 [26:20<05:51, 3.63s/it] {'loss': 1.1376, 'grad_norm': 0.000503327667564786, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:20<05:51, 3.63s/it] 82%|████████▏ | 424/520 [26:24<05:48, 3.63s/it] {'loss': 1.2394, 'grad_norm': 0.0004454474819495372, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:24<05:48, 3.63s/it] 82%|████████▏ | 425/520 [26:28<05:44, 3.63s/it] {'loss': 1.1525, 'grad_norm': 0.0004523979768426005, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:28<05:44, 3.63s/it] 82%|████████▏ | 426/520 [26:31<05:39, 3.62s/it] {'loss': 1.1899, 'grad_norm': 0.0006217556682964412, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:31<05:39, 3.62s/it] 82%|████████▏ | 427/520 [26:35<05:36, 3.62s/it] {'loss': 1.0865, 'grad_norm': 0.00043394206184510796, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:35<05:36, 3.62s/it] 82%|████████▏ | 428/520 [26:39<05:33, 3.62s/it] {'loss': 1.0809, 'grad_norm': 0.0004935554222994951, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:39<05:33, 3.62s/it] 82%|████████▎ | 429/520 [26:42<05:30, 3.63s/it] {'loss': 1.179, 'grad_norm': 0.0005164658012450971, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:42<05:30, 3.63s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:46<05:27, 3.64s/it] {'loss': 1.1809, 'grad_norm': 0.00044844830381276455, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:46<05:27, 3.64s/it] 83%|████████▎ | 431/520 [26:50<05:25, 3.65s/it] {'loss': 1.1298, 'grad_norm': 0.000492662273430053, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:50<05:25, 3.65s/it] 83%|████████▎ | 432/520 [26:53<05:21, 3.65s/it] {'loss': 1.0859, 'grad_norm': 0.000505003990000134, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:53<05:21, 3.65s/it] 83%|████████▎ | 433/520 [26:57<05:18, 3.66s/it] {'loss': 1.2177, 'grad_norm': 0.0004889190403006049, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:57<05:18, 3.66s/it] 83%|████████▎ | 434/520 [27:01<05:17, 3.69s/it] {'loss': 0.9742, 'grad_norm': 0.0004676944616690361, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:01<05:17, 3.69s/it] 84%|████████▎ | 435/520 [27:04<05:16, 3.72s/it] {'loss': 1.2503, 'grad_norm': 0.0005382806747711014, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:04<05:16, 3.72s/it] 84%|████████▍ | 436/520 [27:08<05:15, 3.75s/it] {'loss': 1.0622, 'grad_norm': 0.00048062352163340196, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:08<05:15, 3.75s/it] 84%|████████▍ | 437/520 [27:12<05:13, 3.77s/it] {'loss': 1.2716, 'grad_norm': 0.00045907989545871286, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:12<05:13, 3.77s/it] 84%|████████▍ | 438/520 [27:16<05:09, 3.78s/it] {'loss': 1.0944, 'grad_norm': 0.00047282397663590397, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:16<05:09, 3.78s/it] 84%|████████▍ | 439/520 [27:20<05:07, 3.80s/it] {'loss': 1.1134, 'grad_norm': 0.00037146773603513155, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:20<05:07, 3.80s/it] 85%|████████▍ | 440/520 [27:23<05:03, 3.80s/it] {'loss': 1.1305, 'grad_norm': 0.0005157374382060552, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:24<05:03, 3.80s/it] 85%|████████▍ | 441/520 [27:27<05:01, 3.82s/it] {'loss': 1.1261, 'grad_norm': 0.000540420216787932, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:27<05:01, 3.82s/it] 85%|████████▌ | 442/520 [27:31<04:58, 3.82s/it] {'loss': 1.1908, 'grad_norm': 0.0005234521310429913, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:31<04:58, 3.82s/it] 85%|████████▌ | 443/520 [27:35<04:54, 3.83s/it] {'loss': 1.1998, 'grad_norm': 0.0004505127530503996, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:35<04:54, 3.83s/it] 85%|████████▌ | 444/520 [27:39<04:50, 3.83s/it] {'loss': 1.1681, 'grad_norm': 0.0004139779469598172, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:39<04:50, 3.83s/it] 86%|████████▌ | 445/520 [27:43<04:46, 3.82s/it] {'loss': 1.0953, 'grad_norm': 0.0004429059624981439, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:43<04:46, 3.82s/it] 86%|████████▌ | 446/520 [27:46<04:42, 3.82s/it] {'loss': 1.202, 'grad_norm': 0.0004085144273732426, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:46<04:42, 3.82s/it] 86%|████████▌ | 447/520 [27:50<04:39, 3.82s/it] {'loss': 1.163, 'grad_norm': 0.0004461260164135936, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:50<04:39, 3.82s/it] 86%|████████▌ | 448/520 [27:54<04:34, 3.82s/it] {'loss': 1.1671, 'grad_norm': 0.0005170042030455167, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:54<04:34, 3.82s/it] 86%|████████▋ | 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3.79s/it] 87%|████████▋ | 454/520 [28:17<04:08, 3.77s/it] {'loss': 1.1003, 'grad_norm': 0.0004680042129773707, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:17<04:08, 3.77s/it] 88%|████████▊ | 455/520 [28:21<04:07, 3.80s/it] {'loss': 1.2385, 'grad_norm': 0.0004584621578944625, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:21<04:07, 3.80s/it] 88%|████████▊ | 456/520 [28:25<04:04, 3.83s/it] {'loss': 1.176, 'grad_norm': 0.000497188011778997, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:25<04:04, 3.83s/it] 88%|████████▊ | 457/520 [28:28<04:01, 3.83s/it] {'loss': 1.069, 'grad_norm': 0.00039600110669992314, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:28<04:01, 3.83s/it] 88%|████████▊ | 458/520 [28:32<03:57, 3.84s/it] {'loss': 1.2892, 'grad_norm': 0.0005081374415525421, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 [28:32<03:57, 3.84s/it] 88%|████████▊ | 459/520 [28:36<03:54, 3.85s/it] {'loss': 1.2217, 'grad_norm': 0.0005144193859633972, 'learning_rate': 0.007142160781674645, 'epoch': 0.88} + 88%|████████▊ | 459/520 [28:36<03:54, 3.85s/it] 88%|████████▊ | 460/520 [28:40<03:51, 3.86s/it] {'loss': 1.1159, 'grad_norm': 0.0004633521660112956, 'learning_rate': 0.006912625135579587, 'epoch': 0.88} + 88%|████████▊ | 460/520 [28:40<03:51, 3.86s/it] 89%|████████▊ | 461/520 [28:44<03:48, 3.86s/it] {'loss': 1.149, 'grad_norm': 0.0003738529748616017, 'learning_rate': 0.0066867063190933496, 'epoch': 0.89} + 89%|████████▊ | 461/520 [28:44<03:48, 3.86s/it] 89%|████████▉ | 462/520 [28:48<03:43, 3.86s/it] {'loss': 1.2503, 'grad_norm': 0.0004299708732215236, 'learning_rate': 0.006464413110096601, 'epoch': 0.89} + 89%|████████▉ | 462/520 [28:48<03:43, 3.86s/it] 89%|████████▉ | 463/520 [28:52<03:39, 3.85s/it] {'loss': 1.089, 'grad_norm': 0.0004821700689301973, 'learning_rate': 0.006245754145600091, 'epoch': 0.89} + 89%|████████▉ | 463/520 [28:52<03:39, 3.85s/it] 89%|████████▉ | 464/520 [28:55<03:35, 3.85s/it] {'loss': 1.2059, 'grad_norm': 0.0004727786936354064, 'learning_rate': 0.006030737921409169, 'epoch': 0.89} + 89%|████████▉ | 464/520 [28:55<03:35, 3.85s/it] 89%|████████▉ | 465/520 [28:59<03:31, 3.85s/it] {'loss': 1.3072, 'grad_norm': 0.0004736135859236644, 'learning_rate': 0.005819372791793654, 'epoch': 0.89} + 89%|████████▉ | 465/520 [28:59<03:31, 3.85s/it] 90%|████████▉ | 466/520 [29:03<03:27, 3.84s/it] {'loss': 1.2062, 'grad_norm': 0.0004459941277402007, 'learning_rate': 0.005611666969163243, 'epoch': 0.9} + 90%|████████▉ | 466/520 [29:03<03:27, 3.84s/it] 90%|████████▉ | 467/520 [29:07<03:23, 3.84s/it] {'loss': 1.139, 'grad_norm': 0.0004268989307541067, 'learning_rate': 0.005407628523748398, 'epoch': 0.9} + 90%|████████▉ | 467/520 [29:07<03:23, 3.84s/it] 90%|█████████ | 468/520 [29:11<03:19, 3.83s/it] {'loss': 1.1677, 'grad_norm': 0.0005250343123397134, 'learning_rate': 0.00520726538328683, 'epoch': 0.9} + 90%|█████████ | 468/520 [29:11<03:19, 3.83s/it] 90%|█████████ | 469/520 [29:15<03:15, 3.84s/it] {'loss': 1.2397, 'grad_norm': 0.0006876901441189486, 'learning_rate': 0.005010585332715401, 'epoch': 0.9} + 90%|█████████ | 469/520 [29:15<03:15, 3.84s/it] 90%|█████████ | 470/520 [29:18<03:11, 3.84s/it] {'loss': 1.1111, 'grad_norm': 0.00043005667621010473, 'learning_rate': 0.004817596013867765, 'epoch': 0.9} + 90%|█████████ | 470/520 [29:18<03:11, 3.84s/it] 91%|█████████ | 471/520 [29:22<03:07, 3.83s/it] {'loss': 1.1405, 'grad_norm': 0.000497239999676048, 'learning_rate': 0.004628304925177318, 'epoch': 0.91} + 91%|█████████ | 471/520 [29:22<03:07, 3.83s/it] 91%|█████████ | 472/520 [29:26<03:04, 3.85s/it] {'loss': 1.1093, 'grad_norm': 0.0005185597618582797, 'learning_rate': 0.004442719421385921, 'epoch': 0.91} + 91%|█████████ | 472/520 [29:26<03:04, 3.85s/it] 91%|█████████ | 473/520 [29:30<02:59, 3.83s/it] {'loss': 1.1815, 'grad_norm': 0.0004885373884368492, 'learning_rate': 0.004260846713258193, 'epoch': 0.91} + 91%|█████████ | 473/520 [29:30<02:59, 3.83s/it] 91%|█████████ | 474/520 [29:34<02:53, 3.77s/it] {'loss': 1.1723, 'grad_norm': 0.00042402088922920166, 'learning_rate': 0.004082693867301224, 'epoch': 0.91} + 91%|█████████ | 474/520 [29:34<02:53, 3.77s/it] 91%|█████████▏| 475/520 [29:37<02:47, 3.73s/it] {'loss': 1.0914, 'grad_norm': 0.0004393370972028123, 'learning_rate': 0.003908267805490051, 'epoch': 0.91} + 91%|█████████▏| 475/520 [29:37<02:47, 3.73s/it] 92%|█████████▏| 476/520 [29:41<02:42, 3.69s/it] {'loss': 1.1622, 'grad_norm': 0.000476806923746187, 'learning_rate': 0.003737575304998797, 'epoch': 0.92} + 92%|█████████▏| 476/520 [29:41<02:42, 3.69s/it] 92%|█████████▏| 477/520 [29:45<02:39, 3.70s/it] {'loss': 1.1604, 'grad_norm': 0.0005460499402697687, 'learning_rate': 0.003570622997937234, 'epoch': 0.92} + 92%|█████████▏| 477/520 [29:45<02:39, 3.70s/it] 92%|█████████▏| 478/520 [29:48<02:34, 3.68s/it] 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100%|██████████| 520/520 [32:24<00:00, 3.86s/it] 100%|██████████| 520/520 [32:24<00:00, 3.74s/it] +[2025-10-18 04:46:46,895] [INFO] [launch.py:348:main] Process 942870 exits successfully. +[2025-10-18 04:46:46,895] [INFO] [launch.py:348:main] Process 942867 exits successfully. +[2025-10-18 04:46:46,896] [INFO] [launch.py:348:main] Process 942869 exits successfully. +[2025-10-18 04:46:47,897] [INFO] [launch.py:348:main] Process 942865 exits successfully. +[2025-10-18 04:46:47,898] [INFO] [launch.py:348:main] Process 942868 exits successfully. +[2025-10-18 04:46:47,898] [INFO] [launch.py:348:main] Process 942866 exits successfully. +[2025-10-18 04:46:47,899] [INFO] [launch.py:348:main] Process 942871 exits successfully. +[2025-10-18 04:46:51,904] [INFO] [launch.py:348:main] Process 942864 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.3_2e-1_connector-9.0_2.3_2e-1_ablation_20251018_041251.log +Timestamp: 2025-10-18 04:46:54 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation_20251018_044654.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation_20251018_044654.log new file mode 100644 index 0000000000000000000000000000000000000000..7e7a0fcd97e40dd5d8a7dabd950c7699507c88f9 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation_20251018_044654.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation_20251018_044654.log +Timestamp: 2025-10-18 04:46:54 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:46:57,100] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:00,030] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 04:47:00,032] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 2.5 --temperature_mlp_text 2.5 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 2.5 --temperature_mlp_vision 2.5 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 2.5 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:47:02,636] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:03,689] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 04:47:03,689] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 04:47:03,689] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 04:47:03,689] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 04:47:03,689] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 04:47:03,689] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 04:47:03,689] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 04:47:03,692] [INFO] [launch.py:253:main] process 964932 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', 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'/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', 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'--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:47:03,695] [INFO] [launch.py:253:main] process 964934 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:47:03,697] [INFO] [launch.py:253:main] process 964935 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:47:03,699] [INFO] [launch.py:253:main] process 964936 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', 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'--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:47:03,703] [INFO] [launch.py:253:main] process 964938 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 04:47:03,705] [INFO] [launch.py:253:main] process 964939 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.5', '--temperature_mlp_text', '2.5', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.5', '--temperature_mlp_vision', '2.5', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.5', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 04:47:10,355] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,623] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,658] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,658] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,667] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,670] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,681] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,727] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 04:47:10,773] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,039] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,039] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 04:47:11,071] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,072] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,083] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,084] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,091] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 04:47:11,143] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.5, 'temperature_mlp': 2.5, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.5, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.5, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.5, + "temperature_mlp": 2.5, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:964932:964932 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO NET/IB : No device found. +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO NET/Socket : Using [0]eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Using non-device net plugin version 0 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Using network Socket +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO cudaDriverVersion 12040 +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:964939:964939 [7] NCCL INFO NET/Plugin: Using internal network plugin. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. 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5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read 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21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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[6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964934:966510 [2] NCCL INFO ncclCommInitRank comm 0x556f19b5c240 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964938:966509 [6] NCCL INFO ncclCommInitRank comm 0x55b5e38f0a30 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964936:966503 [4] NCCL INFO ncclCommInitRank comm 0x560693f1fea0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964932:966495 [0] NCCL INFO ncclCommInitRank comm 0x5616cf20bf50 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964937:966498 [5] NCCL INFO ncclCommInitRank comm 0x558c6f5b1680 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964935:966497 [3] NCCL INFO ncclCommInitRank comm 0x55bbac475430 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964933:966504 [1] NCCL INFO ncclCommInitRank comm 0x5624aed5f1b0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xf56daf179c252bbe - Init COMPLETE +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:964939:966496 [7] NCCL INFO ncclCommInitRank comm 0x55a6e6f2adc0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xf56daf179c252bbe - Init COMPLETE +[2025-10-18 04:47:57,445] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 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'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 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'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 04:47:59,235] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 04:48:17,156 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 04:48:17,162 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:004->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read 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all rings +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read 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08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read 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01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:964938:971577 [6] NCCL INFO ncclCommInitRank comm 0x7fb3f406b9a0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964936:971573 [4] NCCL INFO ncclCommInitRank comm 0x7f5e9806aaf0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964934:971578 [2] NCCL INFO ncclCommInitRank comm 0x7f4f6006ada0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964932:971572 [0] NCCL INFO ncclCommInitRank comm 0x7f40e406b260 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964939:971575 [7] NCCL INFO ncclCommInitRank comm 0x7f8a0c06b130 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964937:971576 [5] NCCL INFO ncclCommInitRank comm 0x7fbd5806b3c0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964935:971574 [3] NCCL INFO ncclCommInitRank comm 0x7f1e5006b840 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x75e64406c4990ccf - Init COMPLETE +ywang29-vrdb-test1-worker-0:964933:971579 [1] NCCL INFO ncclCommInitRank comm 0x7f715806acd0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x75e64406c4990ccf - Init COMPLETE + 0%| | 1/520 [00:14<2:03:22, 14.26s/it] {'loss': 2.0514, 'grad_norm': 0.006388305548024638, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:14<2:03:22, 14.26s/it] 0%| | 2/520 [00:18<1:10:15, 8.14s/it] {'loss': 2.0571, 'grad_norm': 0.006922406788255489, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:18<1:10:15, 8.14s/it] 1%| | 3/520 [00:21<53:04, 6.16s/it] {'loss': 2.1972, 'grad_norm': 0.007960964242479945, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<53:04, 6.16s/it] 1%| | 4/520 [00:25<45:06, 5.25s/it] {'loss': 1.9593, 'grad_norm': 0.005623091128670155, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<45:06, 5.25s/it] 1%| | 5/520 [00:29<40:33, 4.73s/it] {'loss': 2.0899, 'grad_norm': 0.00607590719504039, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:29<40:33, 4.73s/it] 1%| | 6/520 [00:33<37:48, 4.41s/it] {'loss': 1.4809, 'grad_norm': 0.0021592950500156282, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:33<37:48, 4.41s/it] 1%|▏ | 7/520 [00:37<35:57, 4.21s/it] {'loss': 1.5555, 'grad_norm': 0.001670577173052501, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:37<35:57, 4.21s/it] 2%|▏ | 8/520 [00:41<36:26, 4.27s/it] {'loss': 1.5169, 'grad_norm': 0.0009578875403885379, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:41<36:26, 4.27s/it] 2%|▏ | 9/520 [00:45<36:36, 4.30s/it] {'loss': 1.6013, 'grad_norm': 0.0010381366485328824, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:45<36:36, 4.30s/it] 2%|▏ | 10/520 [00:49<35:01, 4.12s/it] {'loss': 1.4505, 'grad_norm': 0.0010247892261810816, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:49<35:01, 4.12s/it] 2%|▏ | 11/520 [00:53<34:17, 4.04s/it] {'loss': 1.4838, 'grad_norm': 0.0008228974918970302, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:53<34:17, 4.04s/it] 2%|▏ | 12/520 [00:57<33:16, 3.93s/it] {'loss': 1.349, 'grad_norm': 0.0006093123566018362, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:57<33:16, 3.93s/it][2025-10-18 04:49:23,020] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [01:01<34:18, 4.06s/it] {'loss': 1.4105, 'grad_norm': 0.0006189343699941043, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [01:01<34:18, 4.06s/it] 3%|▎ | 14/520 [01:05<33:29, 3.97s/it] {'loss': 1.4651, 'grad_norm': 0.0007978359428346715, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:05<33:29, 3.97s/it] 3%|▎ | 15/520 [01:09<32:49, 3.90s/it] {'loss': 1.3909, 'grad_norm': 0.0005422544929441823, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:09<32:49, 3.90s/it] 3%|▎ | 16/520 [01:12<32:25, 3.86s/it] {'loss': 1.3588, 'grad_norm': 0.0006305677145975358, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:12<32:25, 3.86s/it] 3%|▎ | 17/520 [01:16<32:04, 3.83s/it] {'loss': 1.4803, 'grad_norm': 0.0006570312731199907, 'learning_rate': 0.1999980572931538, 'epoch': 0.03} + 3%|▎ | 17/520 [01:16<32:04, 3.83s/it] 3%|▎ | 18/520 [01:20<31:51, 3.81s/it] {'loss': 1.3338, 'grad_norm': 0.000631496441207877, 'learning_rate': 0.19999222924809748, 'epoch': 0.03} + 3%|▎ | 18/520 [01:20<31:51, 3.81s/it] 4%|▎ | 19/520 [01:23<31:21, 3.76s/it] {'loss': 1.3451, 'grad_norm': 0.0005608377365764732, 'learning_rate': 0.19998251609127465, 'epoch': 0.04} + 4%|▎ | 19/520 [01:23<31:21, 3.76s/it] 4%|▍ | 20/520 [01:27<30:58, 3.72s/it] {'loss': 1.3066, 'grad_norm': 0.0005710052126098599, 'learning_rate': 0.19996891820008164, 'epoch': 0.04} + 4%|▍ | 20/520 [01:27<30:58, 3.72s/it] 4%|▍ | 21/520 [01:31<30:57, 3.72s/it] {'loss': 1.3335, 'grad_norm': 0.0005524417571363413, 'learning_rate': 0.19995143610285276, 'epoch': 0.04} + 4%|▍ | 21/520 [01:31<30:57, 3.72s/it] 4%|▍ | 22/520 [01:35<30:50, 3.72s/it] {'loss': 1.4446, 'grad_norm': 0.0005406131648381963, 'learning_rate': 0.19993007047883987, 'epoch': 0.04} + 4%|▍ | 22/520 [01:35<30:50, 3.72s/it] 4%|▍ | 23/520 [01:38<30:46, 3.72s/it] {'loss': 1.3972, 'grad_norm': 0.0005799211918825795, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:38<30:46, 3.72s/it] 5%|▍ | 24/520 [01:42<30:48, 3.73s/it] {'loss': 1.3169, 'grad_norm': 0.0005223402889528947, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:42<30:48, 3.73s/it] 5%|▍ | 25/520 [01:46<31:16, 3.79s/it] {'loss': 1.3927, 'grad_norm': 0.0005691562364998686, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:46<31:16, 3.79s/it] 5%|▌ | 26/520 [01:50<31:30, 3.83s/it] {'loss': 1.3341, 'grad_norm': 0.0004452540869502365, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:50<31:30, 3.83s/it] 5%|▌ | 27/520 [01:53<31:01, 3.78s/it] {'loss': 1.2621, 'grad_norm': 0.0004565807317206332, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:53<31:01, 3.78s/it] 5%|▌ | 28/520 [01:57<30:46, 3.75s/it] {'loss': 1.2946, 'grad_norm': 0.0004910146471463065, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:57<30:46, 3.75s/it] 6%|▌ | 29/520 [02:01<30:32, 3.73s/it] {'loss': 1.313, 'grad_norm': 0.0005039278582054179, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [02:01<30:32, 3.73s/it] 6%|▌ | 30/520 [02:05<30:18, 3.71s/it] {'loss': 1.3823, 'grad_norm': 0.0004332805498849874, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:05<30:18, 3.71s/it] 6%|▌ | 31/520 [02:08<30:02, 3.69s/it] {'loss': 1.2755, 'grad_norm': 0.00042571874293945215, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:08<30:02, 3.69s/it] 6%|▌ | 32/520 [02:12<29:59, 3.69s/it] {'loss': 1.2181, 'grad_norm': 0.0005098529938205908, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:12<29:59, 3.69s/it] 6%|▋ | 33/520 [02:16<29:50, 3.68s/it] {'loss': 1.2721, 'grad_norm': 0.0004742605174955881, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:16<29:50, 3.68s/it] 7%|▋ | 34/520 [02:19<29:40, 3.66s/it] {'loss': 1.2668, 'grad_norm': 0.0004967173842453683, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:19<29:40, 3.66s/it] 7%|▋ | 35/520 [02:23<29:37, 3.66s/it] {'loss': 1.2783, 'grad_norm': 0.0005166643344033927, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:23<29:37, 3.66s/it] 7%|▋ | 36/520 [02:26<29:35, 3.67s/it] {'loss': 1.3693, 'grad_norm': 0.00047077399890263483, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:26<29:35, 3.67s/it] 7%|▋ | 37/520 [02:30<29:31, 3.67s/it] {'loss': 1.3556, 'grad_norm': 0.0004464634562487949, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:30<29:31, 3.67s/it] 7%|▋ | 38/520 [02:34<29:25, 3.66s/it] {'loss': 1.4365, 'grad_norm': 0.00044375977746512305, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:34<29:25, 3.66s/it] 8%|▊ | 39/520 [02:37<29:18, 3.66s/it] {'loss': 1.3059, 'grad_norm': 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338/520 [21:12<11:10, 3.69s/it] {'loss': 1.2123, 'grad_norm': 0.0004821253535495942, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:12<11:10, 3.69s/it] 65%|██████▌ | 339/520 [21:16<11:04, 3.67s/it] {'loss': 1.157, 'grad_norm': 0.0005527910818614523, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:16<11:04, 3.67s/it] 65%|██████▌ | 340/520 [21:20<11:01, 3.67s/it] {'loss': 1.1449, 'grad_norm': 0.000484565114772632, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:20<11:01, 3.67s/it] 66%|██████▌ | 341/520 [21:23<10:54, 3.65s/it] {'loss': 1.1747, 'grad_norm': 0.0005386868760792402, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:23<10:54, 3.65s/it] 66%|██████▌ | 342/520 [21:27<10:49, 3.65s/it] {'loss': 1.1884, 'grad_norm': 0.000579466930869939, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:27<10:49, 3.65s/it] 66%|██████▌ | 343/520 [21:31<10:46, 3.65s/it] {'loss': 1.1369, 'grad_norm': 0.0005043762585901572, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:31<10:46, 3.65s/it] 66%|██████▌ | 344/520 [21:34<10:45, 3.67s/it] {'loss': 1.133, 'grad_norm': 0.00046356596315728037, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:34<10:45, 3.67s/it] 66%|██████▋ | 345/520 [21:38<10:40, 3.66s/it] {'loss': 1.2298, 'grad_norm': 0.0004941354979975425, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:38<10:40, 3.66s/it] 67%|██████▋ | 346/520 [21:42<10:37, 3.66s/it] {'loss': 1.1585, 'grad_norm': 0.0005765876216128412, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:42<10:37, 3.66s/it] 67%|██████▋ | 347/520 [21:45<10:33, 3.66s/it] {'loss': 1.1502, 'grad_norm': 0.0004443833170946928, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:45<10:33, 3.66s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:49<10:29, 3.66s/it] {'loss': 1.1059, 'grad_norm': 0.0006255360509693559, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:49<10:29, 3.66s/it] 67%|██████▋ | 349/520 [21:53<10:26, 3.66s/it] {'loss': 1.1422, 'grad_norm': 0.0005160084308108764, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:53<10:26, 3.66s/it] 67%|██████▋ | 350/520 [21:56<10:21, 3.66s/it] {'loss': 1.1875, 'grad_norm': 0.000606793971855404, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:56<10:21, 3.66s/it] 68%|██████▊ | 351/520 [22:00<10:21, 3.68s/it] {'loss': 1.0986, 'grad_norm': 0.00046710912454619405, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [22:00<10:21, 3.68s/it] 68%|██████▊ | 352/520 [22:04<10:16, 3.67s/it] {'loss': 1.2102, 'grad_norm': 0.0004614361936838642, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [22:04<10:16, 3.67s/it] 68%|██████▊ | 353/520 [22:07<10:13, 3.67s/it] {'loss': 1.1324, 'grad_norm': 0.00045089298465880126, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [22:07<10:13, 3.67s/it] 68%|██████▊ | 354/520 [22:11<10:08, 3.67s/it] {'loss': 1.2218, 'grad_norm': 0.0004759452256293145, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:11<10:08, 3.67s/it] 68%|██████▊ | 355/520 [22:15<10:04, 3.66s/it] {'loss': 1.1615, 'grad_norm': 0.0004988658763931018, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:15<10:04, 3.66s/it] 68%|██████▊ | 356/520 [22:18<09:59, 3.65s/it] {'loss': 1.1613, 'grad_norm': 0.0004947321556899124, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:18<09:59, 3.65s/it] 69%|██████▊ | 357/520 [22:22<09:55, 3.65s/it] {'loss': 1.1977, 'grad_norm': 0.000454886288372828, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:22<09:55, 3.65s/it] 69%|██████▉ | 358/520 [22:25<09:51, 3.65s/it] {'loss': 1.1234, 'grad_norm': 0.00047987118170612, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:25<09:51, 3.65s/it] 69%|██████▉ | 359/520 [22:29<09:47, 3.65s/it] {'loss': 1.1649, 'grad_norm': 0.0005103547342582477, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:29<09:47, 3.65s/it] 69%|██████▉ | 360/520 [22:33<09:42, 3.64s/it] {'loss': 1.1713, 'grad_norm': 0.0005873312185389719, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:33<09:42, 3.64s/it] 69%|██████▉ | 361/520 [22:36<09:37, 3.63s/it] {'loss': 1.1909, 'grad_norm': 0.0004289296607695165, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:36<09:37, 3.63s/it] 70%|██████▉ | 362/520 [22:40<09:34, 3.64s/it] {'loss': 1.1702, 'grad_norm': 0.0005239114473997545, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:40<09:34, 3.64s/it] 70%|██████▉ | 363/520 [22:44<09:30, 3.64s/it] {'loss': 1.1997, 'grad_norm': 0.00047243590642607395, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:44<09:30, 3.64s/it] 70%|███████ | 364/520 [22:47<09:28, 3.64s/it] {'loss': 1.2026, 'grad_norm': 0.00047545791687617734, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:47<09:28, 3.64s/it] 70%|███████ | 365/520 [22:51<09:25, 3.65s/it] {'loss': 1.2501, 'grad_norm': 0.0005018454690134175, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:51<09:25, 3.65s/it] 70%|███████ | 366/520 [22:55<09:21, 3.64s/it] {'loss': 1.218, 'grad_norm': 0.00045494870588265376, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:55<09:21, 3.64s/it] 71%|███████ | 367/520 [22:58<09:17, 3.65s/it] {'loss': 1.2171, 'grad_norm': 0.000524542746865856, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:58<09:17, 3.65s/it] 71%|███████ | 368/520 [23:02<09:14, 3.65s/it] {'loss': 1.071, 'grad_norm': 0.0004936379327050419, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [23:02<09:14, 3.65s/it] 71%|███████ | 369/520 [23:06<09:10, 3.64s/it] {'loss': 1.1656, 'grad_norm': 0.0004411883337008739, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [23:06<09:10, 3.64s/it] 71%|███████ | 370/520 [23:09<09:05, 3.64s/it] {'loss': 1.1304, 'grad_norm': 0.00044879854309069794, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:09<09:05, 3.64s/it] 71%|███████▏ | 371/520 [23:13<09:01, 3.63s/it] {'loss': 1.1212, 'grad_norm': 0.000529011947451668, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:13<09:01, 3.63s/it] 72%|███████▏ | 372/520 [23:16<08:58, 3.64s/it] {'loss': 1.2307, 'grad_norm': 0.0005293000009367639, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:16<08:58, 3.64s/it] 72%|███████▏ | 373/520 [23:20<08:55, 3.64s/it] {'loss': 1.121, 'grad_norm': 0.0004961807952095262, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:20<08:55, 3.64s/it] 72%|███████▏ | 374/520 [23:24<08:50, 3.64s/it] {'loss': 1.2173, 'grad_norm': 0.000526377521421033, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:24<08:50, 3.64s/it] 72%|███████▏ | 375/520 [23:27<08:48, 3.65s/it] {'loss': 1.1316, 'grad_norm': 0.0004921257474442477, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:27<08:48, 3.65s/it] 72%|███████▏ | 376/520 [23:31<08:46, 3.66s/it] {'loss': 1.239, 'grad_norm': 0.0004672586288822136, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:31<08:46, 3.66s/it] 72%|███████▎ | 377/520 [23:35<08:41, 3.65s/it] {'loss': 1.1684, 'grad_norm': 0.0005169537061977036, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:35<08:41, 3.65s/it] 73%|███████▎ | 378/520 [23:38<08:36, 3.64s/it] {'loss': 1.2338, 'grad_norm': 0.00046790642840603935, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:38<08:36, 3.64s/it] 73%|███████▎ | 379/520 [23:42<08:32, 3.63s/it] {'loss': 1.1975, 'grad_norm': 0.00046084530766240465, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:42<08:32, 3.63s/it] 73%|███████▎ | 380/520 [23:46<08:28, 3.63s/it] {'loss': 1.21, 'grad_norm': 0.000473426603027029, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:46<08:28, 3.63s/it] 73%|███████▎ | 381/520 [23:49<08:24, 3.63s/it] {'loss': 1.2103, 'grad_norm': 0.0004596037758523463, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:49<08:24, 3.63s/it] 73%|███████▎ | 382/520 [23:53<08:22, 3.64s/it] {'loss': 1.1829, 'grad_norm': 0.00044542989363907545, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:53<08:22, 3.64s/it] 74%|███████▎ | 383/520 [23:56<08:18, 3.64s/it] {'loss': 1.0515, 'grad_norm': 0.000562223766723646, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:56<08:18, 3.64s/it] 74%|███████▍ | 384/520 [24:00<08:15, 3.65s/it] {'loss': 1.2011, 'grad_norm': 0.00044691702569748496, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [24:00<08:15, 3.65s/it] 74%|███████▍ | 385/520 [24:04<08:10, 3.63s/it] {'loss': 1.195, 'grad_norm': 0.00044999218166859393, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [24:04<08:10, 3.63s/it] 74%|███████▍ | 386/520 [24:07<08:07, 3.64s/it] {'loss': 1.1454, 'grad_norm': 0.0004194372978958005, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [24:07<08:07, 3.64s/it] 74%|███████▍ | 387/520 [24:11<08:03, 3.64s/it] {'loss': 1.2287, 'grad_norm': 0.00047829425230009204, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:11<08:03, 3.64s/it] 75%|███████▍ | 388/520 [24:15<07:59, 3.63s/it] {'loss': 1.1075, 'grad_norm': 0.0004683604769518516, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:15<07:59, 3.63s/it] 75%|███████▍ | 389/520 [24:18<07:55, 3.63s/it] {'loss': 1.1544, 'grad_norm': 0.0006535957368109323, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:18<07:55, 3.63s/it] 75%|███████▌ | 390/520 [24:22<07:53, 3.64s/it] {'loss': 1.2204, 'grad_norm': 0.0004752912357787028, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:22<07:53, 3.64s/it] 75%|███████▌ | 391/520 [24:26<07:50, 3.65s/it] {'loss': 1.279, 'grad_norm': 0.0005160394160258877, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:26<07:50, 3.65s/it] 75%|███████▌ | 392/520 [24:29<07:46, 3.65s/it] {'loss': 1.1066, 'grad_norm': 0.00050191451033858, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:29<07:46, 3.65s/it] 76%|███████▌ | 393/520 [24:33<07:44, 3.66s/it] {'loss': 1.094, 'grad_norm': 0.000395815284513087, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:33<07:44, 3.66s/it] 76%|███████▌ | 394/520 [24:37<07:41, 3.66s/it] {'loss': 1.1788, 'grad_norm': 0.0005175262418586775, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:37<07:41, 3.66s/it] 76%|███████▌ | 395/520 [24:40<07:36, 3.65s/it] {'loss': 1.1425, 'grad_norm': 0.0005248402016764319, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:40<07:36, 3.65s/it] 76%|███████▌ | 396/520 [24:44<07:31, 3.64s/it] {'loss': 1.219, 'grad_norm': 0.0005467431168247223, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:44<07:31, 3.64s/it] 76%|███████▋ | 397/520 [24:48<07:28, 3.64s/it] {'loss': 1.1934, 'grad_norm': 0.00045748537092993183, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:48<07:28, 3.64s/it] 77%|███████▋ | 398/520 [24:51<07:24, 3.64s/it] {'loss': 1.19, 'grad_norm': 0.0005059366925839264, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:51<07:24, 3.64s/it] 77%|███████▋ | 399/520 [24:55<07:21, 3.65s/it] {'loss': 1.1241, 'grad_norm': 0.00048141877054235085, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:55<07:21, 3.65s/it] 77%|███████▋ | 400/520 [24:58<07:18, 3.65s/it] {'loss': 1.1569, 'grad_norm': 0.00046257220512556157, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:58<07:18, 3.65s/it] 77%|███████▋ | 401/520 [25:02<07:14, 3.65s/it] {'loss': 1.0301, 'grad_norm': 0.0005485778442144663, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [25:02<07:14, 3.65s/it] 77%|███████▋ | 402/520 [25:06<07:10, 3.65s/it] {'loss': 1.1597, 'grad_norm': 0.0004961864114795769, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [25:06<07:10, 3.65s/it] 78%|███████▊ | 403/520 [25:09<07:05, 3.64s/it] {'loss': 1.1805, 'grad_norm': 0.000516120460858788, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:09<07:05, 3.64s/it] 78%|███████▊ | 404/520 [25:13<07:02, 3.64s/it] {'loss': 1.0936, 'grad_norm': 0.0005602217395038389, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:13<07:02, 3.64s/it] 78%|███████▊ | 405/520 [25:17<07:00, 3.66s/it] {'loss': 1.1386, 'grad_norm': 0.00046320283087727883, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:17<07:00, 3.66s/it] 78%|███████▊ | 406/520 [25:20<06:55, 3.65s/it] {'loss': 1.0623, 'grad_norm': 0.0006861422590086024, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:20<06:55, 3.65s/it] 78%|███████▊ | 407/520 [25:24<06:51, 3.64s/it] {'loss': 1.2562, 'grad_norm': 0.0005321706229016658, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:24<06:51, 3.64s/it] 78%|███████▊ | 408/520 [25:28<06:46, 3.63s/it] {'loss': 1.1743, 'grad_norm': 0.0006239786063718164, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:28<06:46, 3.63s/it] 79%|███████▊ | 409/520 [25:31<06:43, 3.63s/it] {'loss': 1.2868, 'grad_norm': 0.0005910259929801457, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:31<06:43, 3.63s/it] 79%|███████▉ | 410/520 [25:35<06:38, 3.62s/it] {'loss': 1.0316, 'grad_norm': 0.0004988363079049938, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:35<06:38, 3.62s/it] 79%|███████▉ | 411/520 [25:38<06:35, 3.63s/it] {'loss': 1.2693, 'grad_norm': 0.0005195849515441023, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:38<06:35, 3.63s/it] 79%|███████▉ | 412/520 [25:42<06:31, 3.63s/it] {'loss': 1.18, 'grad_norm': 0.0004684770521262329, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:42<06:31, 3.63s/it] 79%|███████▉ | 413/520 [25:46<06:28, 3.63s/it] {'loss': 1.1538, 'grad_norm': 0.00045994952488477976, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:46<06:28, 3.63s/it] 80%|███████▉ | 414/520 [25:49<06:24, 3.63s/it] {'loss': 0.9669, 'grad_norm': 0.000404225539129271, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:49<06:24, 3.63s/it] 80%|███████▉ | 415/520 [25:53<06:20, 3.63s/it] {'loss': 1.1629, 'grad_norm': 0.00046577557126681156, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:53<06:20, 3.63s/it] 80%|████████ | 416/520 [25:57<06:17, 3.63s/it] {'loss': 1.0656, 'grad_norm': 0.0005613837696721001, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:57<06:17, 3.63s/it] 80%|████████ | 417/520 [26:00<06:12, 3.62s/it] {'loss': 1.2278, 'grad_norm': 0.00048235515215009505, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [26:00<06:12, 3.62s/it] 80%|████████ | 418/520 [26:04<06:09, 3.62s/it] {'loss': 1.222, 'grad_norm': 0.0004859508044605803, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [26:04<06:09, 3.62s/it] 81%|████████ | 419/520 [26:07<06:05, 3.62s/it] {'loss': 1.217, 'grad_norm': 0.000523117116485077, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [26:07<06:05, 3.62s/it] 81%|████████ | 420/520 [26:11<06:03, 3.63s/it] {'loss': 1.1078, 'grad_norm': 0.0004967631591622474, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:11<06:03, 3.63s/it] 81%|████████ | 421/520 [26:15<05:59, 3.63s/it] {'loss': 1.0469, 'grad_norm': 0.0005156720058425425, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:15<05:59, 3.63s/it] 81%|████████ | 422/520 [26:18<05:57, 3.65s/it] {'loss': 1.1675, 'grad_norm': 0.0005094602194171209, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:18<05:57, 3.65s/it] 81%|████████▏ | 423/520 [26:22<05:56, 3.67s/it] {'loss': 1.1332, 'grad_norm': 0.0005446492651418907, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:22<05:56, 3.67s/it] 82%|████████▏ | 424/520 [26:26<05:56, 3.71s/it] {'loss': 1.2362, 'grad_norm': 0.0004917678183812695, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:26<05:56, 3.71s/it] 82%|████████▏ | 425/520 [26:30<05:49, 3.68s/it] {'loss': 1.1502, 'grad_norm': 0.0004911389067355439, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:30<05:49, 3.68s/it] 82%|████████▏ | 426/520 [26:33<05:45, 3.67s/it] {'loss': 1.185, 'grad_norm': 0.0006650387403587919, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:33<05:45, 3.67s/it] 82%|████████▏ | 427/520 [26:37<05:44, 3.70s/it] {'loss': 1.083, 'grad_norm': 0.00045331548073288794, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:37<05:44, 3.70s/it] 82%|████████▏ | 428/520 [26:41<05:45, 3.76s/it] {'loss': 1.0761, 'grad_norm': 0.0005127336696564314, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:41<05:45, 3.76s/it] 82%|████████▎ | 429/520 [26:45<05:44, 3.79s/it] {'loss': 1.1746, 'grad_norm': 0.0005484795263403287, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:45<05:44, 3.79s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:49<05:42, 3.81s/it] {'loss': 1.1771, 'grad_norm': 0.0004756731217772915, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:49<05:42, 3.81s/it] 83%|████████▎ | 431/520 [26:52<05:40, 3.83s/it] {'loss': 1.1259, 'grad_norm': 0.0005195990101282164, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:52<05:40, 3.83s/it] 83%|████████▎ | 432/520 [26:56<05:37, 3.84s/it] {'loss': 1.0808, 'grad_norm': 0.0005157177830171551, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:56<05:37, 3.84s/it] 83%|████████▎ | 433/520 [27:00<05:34, 3.84s/it] {'loss': 1.214, 'grad_norm': 0.0005318461666728468, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [27:00<05:34, 3.84s/it] 83%|████████▎ | 434/520 [27:04<05:31, 3.86s/it] {'loss': 0.9688, 'grad_norm': 0.00048393097000547404, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [27:04<05:31, 3.86s/it] 84%|████████▎ | 435/520 [27:08<05:27, 3.86s/it] {'loss': 1.246, 'grad_norm': 0.0005587246559352765, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [27:08<05:27, 3.86s/it] 84%|████████▍ | 436/520 [27:12<05:23, 3.86s/it] {'loss': 1.0573, 'grad_norm': 0.0004970062417449652, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [27:12<05:23, 3.86s/it] 84%|████████▍ | 437/520 [27:16<05:20, 3.86s/it] {'loss': 1.2662, 'grad_norm': 0.0004783268063118896, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:16<05:20, 3.86s/it] 84%|████████▍ | 438/520 [27:19<05:15, 3.85s/it] {'loss': 1.0907, 'grad_norm': 0.0004906521325194488, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:19<05:15, 3.85s/it] 84%|████████▍ | 439/520 [27:23<05:12, 3.86s/it] {'loss': 1.1103, 'grad_norm': 0.0003849627070111919, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:23<05:12, 3.86s/it] 85%|████████▍ | 440/520 [27:27<05:09, 3.87s/it] {'loss': 1.1253, 'grad_norm': 0.0005381473704897803, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:27<05:09, 3.87s/it] 85%|████████▍ | 441/520 [27:31<05:05, 3.87s/it] {'loss': 1.1225, 'grad_norm': 0.0006064263560692552, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:31<05:05, 3.87s/it] 85%|████████▌ | 442/520 [27:35<05:00, 3.86s/it] {'loss': 1.1876, 'grad_norm': 0.0005488704145473497, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:35<05:00, 3.86s/it] 85%|████████▌ | 443/520 [27:39<04:57, 3.86s/it] {'loss': 1.1964, 'grad_norm': 0.00047781876452884656, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:39<04:57, 3.86s/it] 85%|████████▌ | 444/520 [27:43<04:54, 3.87s/it] {'loss': 1.1631, 'grad_norm': 0.0004290669210880998, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:43<04:54, 3.87s/it] 86%|████████▌ | 445/520 [27:47<04:49, 3.86s/it] {'loss': 1.0909, 'grad_norm': 0.0004657645357948613, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:47<04:49, 3.86s/it] 86%|████████▌ | 446/520 [27:50<04:40, 3.79s/it] {'loss': 1.2, 'grad_norm': 0.00042823128973033044, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:50<04:40, 3.79s/it] 86%|████████▌ | 447/520 [27:54<04:33, 3.75s/it] {'loss': 1.1594, 'grad_norm': 0.0004642122700609572, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:54<04:33, 3.75s/it] 86%|████████▌ | 448/520 [27:57<04:28, 3.72s/it] {'loss': 1.1634, 'grad_norm': 0.0005391493213035734, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:57<04:28, 3.72s/it] 86%|████████▋ | 449/520 [28:01<04:22, 3.70s/it] {'loss': 1.1616, 'grad_norm': 0.00046867045028479553, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [28:01<04:22, 3.70s/it] 87%|████████▋ | 450/520 [28:05<04:16, 3.66s/it] {'loss': 1.1839, 'grad_norm': 0.0004803008070235819, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [28:05<04:16, 3.66s/it] 87%|████████▋ | 451/520 [28:08<04:12, 3.66s/it] {'loss': 1.1874, 'grad_norm': 0.0004945029527745971, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [28:08<04:12, 3.66s/it] 87%|████████▋ | 452/520 [28:12<04:07, 3.64s/it] {'loss': 1.206, 'grad_norm': 0.0004601200876180581, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [28:12<04:07, 3.64s/it] 87%|████████▋ | 453/520 [28:16<04:03, 3.64s/it] {'loss': 1.1831, 'grad_norm': 0.0004816218063944766, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:16<04:03, 3.64s/it] 87%|████████▋ | 454/520 [28:19<04:00, 3.64s/it] {'loss': 1.096, 'grad_norm': 0.0004955296518979571, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:19<04:00, 3.64s/it] 88%|████████▊ | 455/520 [28:23<03:56, 3.65s/it] {'loss': 1.234, 'grad_norm': 0.00048013015628360726, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:23<03:56, 3.65s/it] 88%|████████▊ | 456/520 [28:27<03:57, 3.71s/it] {'loss': 1.1709, 'grad_norm': 0.0005128346515837012, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:27<03:57, 3.71s/it] 88%|████████▊ | 457/520 [28:31<03:57, 3.76s/it] {'loss': 1.0663, 'grad_norm': 0.0004099562319027162, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:31<03:57, 3.76s/it] 88%|████████▊ | 458/520 [28:35<03:55, 3.80s/it] {'loss': 1.2852, 'grad_norm': 0.0005400403107360807, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 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520/520 [32:31<00:00, 3.83s/it] 100%|██████████| 520/520 [32:31<00:00, 3.75s/it] +[2025-10-18 05:20:58,873] [INFO] [launch.py:348:main] Process 964933 exits successfully. +[2025-10-18 05:20:58,874] [INFO] [launch.py:348:main] Process 964936 exits successfully. +[2025-10-18 05:20:59,876] [INFO] [launch.py:348:main] Process 964938 exits successfully. +[2025-10-18 05:20:59,877] [INFO] [launch.py:348:main] Process 964935 exits successfully. +[2025-10-18 05:20:59,878] [INFO] [launch.py:348:main] Process 964937 exits successfully. +[2025-10-18 05:20:59,878] [INFO] [launch.py:348:main] Process 964934 exits successfully. +[2025-10-18 05:20:59,879] [INFO] [launch.py:348:main] Process 964939 exits successfully. +[2025-10-18 05:21:03,884] [INFO] [launch.py:348:main] Process 964932 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.5_2e-1_connector-9.0_2.5_2e-1_ablation_20251018_044654.log +Timestamp: 2025-10-18 05:21:06 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation_20251018_052106.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation_20251018_052106.log new file mode 100644 index 0000000000000000000000000000000000000000..7d4c0823df4cd9cb87e6341e9206fb505797a5b9 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation_20251018_052106.log @@ -0,0 +1,2312 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation_20251018_052106.log +Timestamp: 2025-10-18 05:21:06 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:21:09,142] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:11,868] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 05:21:11,870] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 2.7 --temperature_mlp_text 2.7 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 2.7 --temperature_mlp_vision 2.7 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 2.7 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:21:14,425] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:15,455] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 05:21:15,455] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 05:21:15,455] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 05:21:15,455] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 05:21:15,455] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 05:21:15,455] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 05:21:15,455] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 05:21:15,457] [INFO] [launch.py:253:main] process 987041 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,459] [INFO] [launch.py:253:main] process 987042 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,461] [INFO] [launch.py:253:main] process 987043 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,463] [INFO] [launch.py:253:main] process 987044 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,465] [INFO] [launch.py:253:main] process 987045 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,467] [INFO] [launch.py:253:main] process 987046 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,469] [INFO] [launch.py:253:main] process 987047 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:21:15,471] [INFO] [launch.py:253:main] process 987048 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.7', '--temperature_mlp_text', '2.7', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.7', '--temperature_mlp_vision', '2.7', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.7', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:21:22,252] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,663] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:22,711] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,775] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,775] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,779] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,830] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,830] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:22,831] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:21:23,113] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,177] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,179] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,188] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,188] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 05:21:23,225] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,226] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:21:23,229] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.7, 'temperature_mlp': 2.7, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.7, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.7, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.7, + "temperature_mlp": 2.7, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. 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2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read 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23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987041:988613 [0] NCCL INFO ncclCommInitRank comm 0x55c8b37a13c0 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987042:988618 [1] NCCL INFO ncclCommInitRank comm 0x5580a08c5b30 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987043:988617 [2] NCCL INFO ncclCommInitRank comm 0x5582874b4940 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987044:988616 [3] NCCL INFO ncclCommInitRank comm 0x55b35124dac0 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO TUNER/Plugin: Using internal tuner plugin. +ywang29-vrdb-test1-worker-0:987047:988615 [6] NCCL INFO ncclCommInitRank comm 0x55afca3c1200 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987045:988614 [4] NCCL INFO ncclCommInitRank comm 0x562ac5940400 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987048:988620 [7] NCCL INFO ncclCommInitRank comm 0x562f99ec6fd0 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +ywang29-vrdb-test1-worker-0:987046:988619 [5] NCCL INFO ncclCommInitRank comm 0x56394be833e0 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0xdfa8d79eaece01c6 - Init COMPLETE +[2025-10-18 05:22:07,566] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 459, num_elems = 0.99B +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 05:22:09,321] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 05:22:27,367 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 05:22:27,373 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:006->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read 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20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read 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22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read 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15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read 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NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read 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22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read 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00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read 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18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO Connected all trees 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trees +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:987044:993617 [3] NCCL INFO ncclCommInitRank comm 0x7f70f806b970 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 201d0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987048:993618 [7] NCCL INFO ncclCommInitRank comm 0x7fca7006ab50 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId a01d0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987045:993614 [4] NCCL INFO ncclCommInitRank comm 0x7f9cc406b4a0 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987047:993620 [6] NCCL INFO ncclCommInitRank comm 0x7f97b006a950 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987043:993615 [2] NCCL INFO ncclCommInitRank comm 0x7f61c006b050 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 201c0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987041:993613 [0] NCCL INFO ncclCommInitRank comm 0x7fbd4806b870 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 101c0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987042:993616 [1] NCCL INFO ncclCommInitRank comm 0x7f1b7406b910 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 101d0 commId 0x842979570fc56f7e - Init COMPLETE +ywang29-vrdb-test1-worker-0:987046:993619 [5] NCCL INFO ncclCommInitRank comm 0x7fa1d406ba80 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 901d0 commId 0x842979570fc56f7e - Init COMPLETE + 0%| | 1/520 [00:13<2:00:12, 13.90s/it] {'loss': 2.0564, 'grad_norm': 0.007518179361898644, 'learning_rate': 0.0125, 'epoch': 0.0} + 0%| | 1/520 [00:13<2:00:12, 13.90s/it] 0%| | 2/520 [00:17<1:07:41, 7.84s/it] {'loss': 2.0612, 'grad_norm': 0.008117466222935544, 'learning_rate': 0.025, 'epoch': 0.0} + 0%| | 2/520 [00:17<1:07:41, 7.84s/it] 1%| | 3/520 [00:21<50:54, 5.91s/it] {'loss': 2.2037, 'grad_norm': 0.00936468624726689, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<50:54, 5.91s/it] 1%| | 4/520 [00:24<42:57, 5.00s/it] {'loss': 1.9671, 'grad_norm': 0.006650130901307463, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:24<42:57, 5.00s/it] 1%| | 5/520 [00:28<38:35, 4.50s/it] {'loss': 2.0995, 'grad_norm': 0.00730295601974741, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<38:35, 4.50s/it] 1%| | 6/520 [00:31<36:00, 4.20s/it] {'loss': 1.4806, 'grad_norm': 0.002358818361825179, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:31<36:00, 4.20s/it] 1%|▏ | 7/520 [00:35<34:14, 4.00s/it] {'loss': 1.5598, 'grad_norm': 0.0020725358395097854, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:35<34:14, 4.00s/it] 2%|▏ | 8/520 [00:39<34:56, 4.09s/it] {'loss': 1.5177, 'grad_norm': 0.0011717329898620022, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:39<34:56, 4.09s/it] 2%|▏ | 9/520 [00:44<35:09, 4.13s/it] {'loss': 1.6048, 'grad_norm': 0.0012773702455728306, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<35:09, 4.13s/it] 2%|▏ | 10/520 [00:47<33:54, 3.99s/it] {'loss': 1.4524, 'grad_norm': 0.0012466606596946762, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:47<33:54, 3.99s/it] 2%|▏ | 11/520 [00:51<33:32, 3.95s/it] {'loss': 1.4668, 'grad_norm': 0.0008969984220910115, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:51<33:32, 3.95s/it] 2%|▏ | 12/520 [00:55<32:59, 3.90s/it] {'loss': 1.3526, 'grad_norm': 0.0008552072213098294, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<32:59, 3.90s/it][2025-10-18 05:23:31,461] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. If you are unable to make the cache flushes go away consider adding get_accelerator().empty_cache() calls in your training loop to ensure that all ranks flush their caches at the same time + 2%|▎ | 13/520 [00:59<34:11, 4.05s/it] {'loss': 1.4171, 'grad_norm': 0.0007818633494197179, 'learning_rate': 0.1625, 'epoch': 0.03} + 2%|▎ | 13/520 [00:59<34:11, 4.05s/it] 3%|▎ | 14/520 [01:03<33:23, 3.96s/it] {'loss': 1.4628, 'grad_norm': 0.0008236060877416573, 'learning_rate': 0.17500000000000002, 'epoch': 0.03} + 3%|▎ | 14/520 [01:03<33:23, 3.96s/it] 3%|▎ | 15/520 [01:07<32:47, 3.90s/it] {'loss': 1.3963, 'grad_norm': 0.0006698846392294723, 'learning_rate': 0.1875, 'epoch': 0.03} + 3%|▎ | 15/520 [01:07<32:47, 3.90s/it] 3%|▎ | 16/520 [01:10<32:08, 3.83s/it] {'loss': 1.3587, 'grad_norm': 0.0008159486821445489, 'learning_rate': 0.2, 'epoch': 0.03} + 3%|▎ | 16/520 [01:10<32:08, 3.83s/it] 3%|▎ | 17/520 [01:14<31:59, 3.82s/it] {'loss': 1.4711, 'grad_norm': 0.000764819918646243, 'learning_rate': 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23/520 [01:36<30:06, 3.64s/it] {'loss': 1.3956, 'grad_norm': 0.0006279215923447317, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:36<30:06, 3.64s/it] 5%|▍ | 24/520 [01:39<29:55, 3.62s/it] {'loss': 1.3207, 'grad_norm': 0.0006250686349978365, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<29:55, 3.62s/it] 5%|▍ | 25/520 [01:43<29:50, 3.62s/it] {'loss': 1.395, 'grad_norm': 0.0007304554891347202, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:43<29:50, 3.62s/it] 5%|▌ | 26/520 [01:47<29:49, 3.62s/it] {'loss': 1.3352, 'grad_norm': 0.0005312077779728108, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:47<29:49, 3.62s/it] 5%|▌ | 27/520 [01:50<29:42, 3.61s/it] {'loss': 1.2636, 'grad_norm': 0.0005403156824422385, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:50<29:42, 3.61s/it] 5%|▌ | 28/520 [01:54<29:38, 3.61s/it] {'loss': 1.2921, 'grad_norm': 0.0005982424833936564, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:54<29:38, 3.61s/it] 6%|▌ | 29/520 [01:58<29:35, 3.62s/it] {'loss': 1.312, 'grad_norm': 0.0005683350767219933, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:58<29:35, 3.62s/it] 6%|▌ | 30/520 [02:01<29:33, 3.62s/it] {'loss': 1.3836, 'grad_norm': 0.0005042598508830957, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:01<29:33, 3.62s/it] 6%|▌ | 31/520 [02:05<29:30, 3.62s/it] {'loss': 1.2742, 'grad_norm': 0.000497956718572219, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:05<29:30, 3.62s/it] 6%|▌ | 32/520 [02:08<29:27, 3.62s/it] {'loss': 1.2228, 'grad_norm': 0.0005789248738482631, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:08<29:27, 3.62s/it] 6%|▋ | 33/520 [02:12<29:23, 3.62s/it] {'loss': 1.2735, 'grad_norm': 0.0005613531728306351, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:12<29:23, 3.62s/it] 7%|▋ | 34/520 [02:16<29:23, 3.63s/it] {'loss': 1.2646, 'grad_norm': 0.0005867921217707931, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:16<29:23, 3.63s/it] 7%|▋ | 35/520 [02:19<29:16, 3.62s/it] {'loss': 1.2774, 'grad_norm': 0.0006075864557904992, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:19<29:16, 3.62s/it] 7%|▋ | 36/520 [02:23<29:09, 3.61s/it] {'loss': 1.3671, 'grad_norm': 0.0005472935226481521, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:23<29:09, 3.61s/it] 7%|▋ | 37/520 [02:27<29:03, 3.61s/it] {'loss': 1.354, 'grad_norm': 0.0005097385341149894, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:27<29:03, 3.61s/it] 7%|▋ | 38/520 [02:30<28:58, 3.61s/it] {'loss': 1.4372, 'grad_norm': 0.0005171511944585734, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:30<28:58, 3.61s/it] 8%|▊ | 39/520 [02:34<28:51, 3.60s/it] {'loss': 1.3015, 'grad_norm': 0.0006353941984458235, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:34<28:51, 3.60s/it] 8%|▊ | 40/520 [02:37<28:48, 3.60s/it] {'loss': 1.3327, 'grad_norm': 0.0005010571391589858, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:37<28:48, 3.60s/it] 8%|▊ | 41/520 [02:41<28:44, 3.60s/it] {'loss': 1.3121, 'grad_norm': 0.0005520423120475639, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:41<28:44, 3.60s/it] 8%|▊ | 42/520 [02:44<28:39, 3.60s/it] {'loss': 1.3038, 'grad_norm': 0.0006516193116555977, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:44<28:39, 3.60s/it] 8%|▊ | 43/520 [02:48<28:40, 3.61s/it] {'loss': 1.2446, 'grad_norm': 0.0004944095375397767, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:48<28:40, 3.61s/it] 8%|▊ | 44/520 [02:52<28:33, 3.60s/it] {'loss': 1.3468, 'grad_norm': 0.0005165152632105945, 'learning_rate': 0.19848077530122082, 'epoch': 0.08} + 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{'loss': 1.3203, 'grad_norm': 0.0005241961545380823, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:13<28:05, 3.59s/it] 10%|▉ | 51/520 [03:17<27:57, 3.58s/it] {'loss': 1.2587, 'grad_norm': 0.0005924378452717625, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:17<27:57, 3.58s/it] 10%|█ | 52/520 [03:20<27:55, 3.58s/it] {'loss': 1.3857, 'grad_norm': 0.0006814358311326245, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:20<27:55, 3.58s/it] 10%|█ | 53/520 [03:24<27:50, 3.58s/it] {'loss': 1.3642, 'grad_norm': 0.0005553262332699832, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:24<27:50, 3.58s/it] 10%|█ | 54/520 [03:27<27:43, 3.57s/it] {'loss': 1.3019, 'grad_norm': 0.0005282261782540606, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:27<27:43, 3.57s/it] 11%|█ | 55/520 [03:31<27:43, 3.58s/it] {'loss': 1.2554, 'grad_norm': 0.0006289459139332598, 'learning_rate': 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0.0005130546807006588, 'learning_rate': 0.05773817382593008, 'epoch': 0.65} + 65%|██████▌ | 338/520 [21:04<10:59, 3.63s/it] 65%|██████▌ | 339/520 [21:08<10:54, 3.61s/it] {'loss': 1.1541, 'grad_norm': 0.0005821330826500093, 'learning_rate': 0.057174067974773715, 'epoch': 0.65} + 65%|██████▌ | 339/520 [21:08<10:54, 3.61s/it] 65%|██████▌ | 340/520 [21:12<10:51, 3.62s/it] {'loss': 1.1446, 'grad_norm': 0.0005202044348202424, 'learning_rate': 0.056611626088244195, 'epoch': 0.65} + 65%|██████▌ | 340/520 [21:12<10:51, 3.62s/it] 66%|██████▌ | 341/520 [21:15<10:46, 3.61s/it] {'loss': 1.1724, 'grad_norm': 0.0005859361354072063, 'learning_rate': 0.056050870019535494, 'epoch': 0.66} + 66%|██████▌ | 341/520 [21:15<10:46, 3.61s/it] 66%|██████▌ | 342/520 [21:19<10:42, 3.61s/it] {'loss': 1.187, 'grad_norm': 0.0006082874158144035, 'learning_rate': 0.05549182155634076, 'epoch': 0.66} + 66%|██████▌ | 342/520 [21:19<10:42, 3.61s/it] 66%|██████▌ | 343/520 [21:22<10:38, 3.61s/it] {'loss': 1.1364, 'grad_norm': 0.0005684496197001074, 'learning_rate': 0.054934502420005464, 'epoch': 0.66} + 66%|██████▌ | 343/520 [21:22<10:38, 3.61s/it] 66%|██████▌ | 344/520 [21:26<10:34, 3.61s/it] {'loss': 1.131, 'grad_norm': 0.0004950857599471983, 'learning_rate': 0.0543789342646837, 'epoch': 0.66} + 66%|██████▌ | 344/520 [21:26<10:34, 3.61s/it] 66%|██████▋ | 345/520 [21:30<10:32, 3.61s/it] {'loss': 1.2272, 'grad_norm': 0.0005437885881323248, 'learning_rate': 0.05382513867649663, 'epoch': 0.66} + 66%|██████▋ | 345/520 [21:30<10:32, 3.61s/it] 67%|██████▋ | 346/520 [21:33<10:28, 3.61s/it] {'loss': 1.1595, 'grad_norm': 0.0006405169273164586, 'learning_rate': 0.0532731371726938, 'epoch': 0.67} + 67%|██████▋ | 346/520 [21:33<10:28, 3.61s/it] 67%|██████▋ | 347/520 [21:37<10:22, 3.60s/it] {'loss': 1.1479, 'grad_norm': 0.00048768345800512144, 'learning_rate': 0.05272295120081732, 'epoch': 0.67} + 67%|██████▋ | 347/520 [21:37<10:22, 3.60s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2778 > 2048). Running this sequence through the model will result in indexing errors + 67%|██████▋ | 348/520 [21:40<10:19, 3.60s/it] {'loss': 1.1038, 'grad_norm': 0.0006852472581270897, 'learning_rate': 0.05217460213786821, 'epoch': 0.67} + 67%|██████▋ | 348/520 [21:40<10:19, 3.60s/it] 67%|██████▋ | 349/520 [21:44<10:15, 3.60s/it] {'loss': 1.1392, 'grad_norm': 0.0005238130271259587, 'learning_rate': 0.051628111289476024, 'epoch': 0.67} + 67%|██████▋ | 349/520 [21:44<10:15, 3.60s/it] 67%|██████▋ | 350/520 [21:48<10:12, 3.61s/it] {'loss': 1.1854, 'grad_norm': 0.0006707851662563821, 'learning_rate': 0.051083499889071106, 'epoch': 0.67} + 67%|██████▋ | 350/520 [21:48<10:12, 3.61s/it] 68%|██████▊ | 351/520 [21:51<10:11, 3.62s/it] {'loss': 1.0974, 'grad_norm': 0.0005106997199672705, 'learning_rate': 0.05054078909705926, 'epoch': 0.68} + 68%|██████▊ | 351/520 [21:51<10:11, 3.62s/it] 68%|██████▊ | 352/520 [21:55<10:07, 3.62s/it] {'loss': 1.209, 'grad_norm': 0.0005019731356080437, 'learning_rate': 0.050000000000000024, 'epoch': 0.68} + 68%|██████▊ | 352/520 [21:55<10:07, 3.62s/it] 68%|██████▊ | 353/520 [21:58<10:05, 3.63s/it] {'loss': 1.1312, 'grad_norm': 0.00047858153323285404, 'learning_rate': 0.04946115360978696, 'epoch': 0.68} + 68%|██████▊ | 353/520 [21:58<10:05, 3.63s/it] 68%|██████▊ | 354/520 [22:02<10:02, 3.63s/it] {'loss': 1.2218, 'grad_norm': 0.0005119055212867093, 'learning_rate': 0.048924270862831465, 'epoch': 0.68} + 68%|██████▊ | 354/520 [22:02<10:02, 3.63s/it] 68%|██████▊ | 355/520 [22:06<09:57, 3.62s/it] {'loss': 1.1584, 'grad_norm': 0.0005366652862447861, 'learning_rate': 0.04838937261924933, 'epoch': 0.68} + 68%|██████▊ | 355/520 [22:06<09:57, 3.62s/it] 68%|██████▊ | 356/520 [22:09<09:53, 3.62s/it] {'loss': 1.1587, 'grad_norm': 0.000535400720247937, 'learning_rate': 0.0478564796620502, 'epoch': 0.68} + 68%|██████▊ | 356/520 [22:09<09:53, 3.62s/it] 69%|██████▊ | 357/520 [22:13<09:49, 3.62s/it] {'loss': 1.1939, 'grad_norm': 0.0004928483051276202, 'learning_rate': 0.04732561269632993, 'epoch': 0.69} + 69%|██████▊ | 357/520 [22:13<09:49, 3.62s/it] 69%|██████▉ | 358/520 [22:17<09:46, 3.62s/it] {'loss': 1.1205, 'grad_norm': 0.0005208618931665446, 'learning_rate': 0.04679679234846636, 'epoch': 0.69} + 69%|██████▉ | 358/520 [22:17<09:46, 3.62s/it] 69%|██████▉ | 359/520 [22:20<09:43, 3.62s/it] {'loss': 1.164, 'grad_norm': 0.0005592596886767767, 'learning_rate': 0.046270039165317606, 'epoch': 0.69} + 69%|██████▉ | 359/520 [22:20<09:43, 3.62s/it] 69%|██████▉ | 360/520 [22:24<09:39, 3.62s/it] {'loss': 1.1704, 'grad_norm': 0.0006562081624668674, 'learning_rate': 0.04574537361342407, 'epoch': 0.69} + 69%|██████▉ | 360/520 [22:24<09:39, 3.62s/it] 69%|██████▉ | 361/520 [22:27<09:39, 3.64s/it] {'loss': 1.1902, 'grad_norm': 0.00047734955791405557, 'learning_rate': 0.04522281607821288, 'epoch': 0.69} + 69%|██████▉ | 361/520 [22:27<09:39, 3.64s/it] 70%|██████▉ | 362/520 [22:31<09:32, 3.62s/it] {'loss': 1.1678, 'grad_norm': 0.0005732848706568411, 'learning_rate': 0.04470238686320606, 'epoch': 0.7} + 70%|██████▉ | 362/520 [22:31<09:32, 3.62s/it] 70%|██████▉ | 363/520 [22:35<09:28, 3.62s/it] {'loss': 1.1961, 'grad_norm': 0.0005125789067025015, 'learning_rate': 0.044184106189231624, 'epoch': 0.7} + 70%|██████▉ | 363/520 [22:35<09:28, 3.62s/it] 70%|███████ | 364/520 [22:38<09:25, 3.62s/it] {'loss': 1.2025, 'grad_norm': 0.0005330842679609422, 'learning_rate': 0.043667994193637795, 'epoch': 0.7} + 70%|███████ | 364/520 [22:38<09:25, 3.62s/it] 70%|███████ | 365/520 [22:42<09:20, 3.62s/it] {'loss': 1.2481, 'grad_norm': 0.0005430548136846386, 'learning_rate': 0.043154070929510784, 'epoch': 0.7} + 70%|███████ | 365/520 [22:42<09:20, 3.62s/it] 70%|███████ | 366/520 [22:46<09:17, 3.62s/it] {'loss': 1.2148, 'grad_norm': 0.0004955816412892685, 'learning_rate': 0.04264235636489542, 'epoch': 0.7} + 70%|███████ | 366/520 [22:46<09:17, 3.62s/it] 71%|███████ | 367/520 [22:49<09:16, 3.63s/it] {'loss': 1.2138, 'grad_norm': 0.0005447071819053896, 'learning_rate': 0.04213287038201943, 'epoch': 0.71} + 71%|███████ | 367/520 [22:49<09:16, 3.63s/it] 71%|███████ | 368/520 [22:53<09:12, 3.63s/it] {'loss': 1.0681, 'grad_norm': 0.0005375956049081269, 'learning_rate': 0.04162563277652104, 'epoch': 0.71} + 71%|███████ | 368/520 [22:53<09:12, 3.63s/it] 71%|███████ | 369/520 [22:56<09:07, 3.62s/it] {'loss': 1.1652, 'grad_norm': 0.000483449502219094, 'learning_rate': 0.04112066325667954, 'epoch': 0.71} + 71%|███████ | 369/520 [22:56<09:07, 3.62s/it] 71%|███████ | 370/520 [23:00<09:04, 3.63s/it] {'loss': 1.1272, 'grad_norm': 0.0004883131961024292, 'learning_rate': 0.04061798144264986, 'epoch': 0.71} + 71%|███████ | 370/520 [23:00<09:04, 3.63s/it] 71%|███████▏ | 371/520 [23:04<08:59, 3.62s/it] {'loss': 1.1199, 'grad_norm': 0.0005678410056242318, 'learning_rate': 0.04011760686569998, 'epoch': 0.71} + 71%|███████▏ | 371/520 [23:04<08:59, 3.62s/it] 72%|███████▏ | 372/520 [23:07<08:57, 3.63s/it] {'loss': 1.2308, 'grad_norm': 0.000558149471917912, 'learning_rate': 0.03961955896745224, 'epoch': 0.72} + 72%|███████▏ | 372/520 [23:07<08:57, 3.63s/it] 72%|███████▏ | 373/520 [23:11<08:54, 3.63s/it] {'loss': 1.1212, 'grad_norm': 0.0005392157966907312, 'learning_rate': 0.03912385709912794, 'epoch': 0.72} + 72%|███████▏ | 373/520 [23:11<08:54, 3.63s/it] 72%|███████▏ | 374/520 [23:15<08:49, 3.63s/it] {'loss': 1.2137, 'grad_norm': 0.0005668134211919233, 'learning_rate': 0.038630520520795276, 'epoch': 0.72} + 72%|███████▏ | 374/520 [23:15<08:49, 3.63s/it] 72%|███████▏ | 375/520 [23:18<08:46, 3.63s/it] {'loss': 1.1303, 'grad_norm': 0.0005353480597596926, 'learning_rate': 0.03813956840062119, 'epoch': 0.72} + 72%|███████▏ | 375/520 [23:18<08:46, 3.63s/it] 72%|███████▏ | 376/520 [23:22<08:42, 3.63s/it] {'loss': 1.2375, 'grad_norm': 0.0005173723116391745, 'learning_rate': 0.037651019814126656, 'epoch': 0.72} + 72%|███████▏ | 376/520 [23:22<08:42, 3.63s/it] 72%|███████▎ | 377/520 [23:26<08:38, 3.63s/it] {'loss': 1.1665, 'grad_norm': 0.000548422078872614, 'learning_rate': 0.037164893743445275, 'epoch': 0.72} + 72%|███████▎ | 377/520 [23:26<08:38, 3.63s/it] 73%|███████▎ | 378/520 [23:29<08:34, 3.63s/it] {'loss': 1.232, 'grad_norm': 0.0005169207921446493, 'learning_rate': 0.03668120907658603, 'epoch': 0.73} + 73%|███████▎ | 378/520 [23:29<08:34, 3.63s/it] 73%|███████▎ | 379/520 [23:33<08:30, 3.62s/it] {'loss': 1.1954, 'grad_norm': 0.000507044025347812, 'learning_rate': 0.036199984606699154, 'epoch': 0.73} + 73%|███████▎ | 379/520 [23:33<08:30, 3.62s/it] 73%|███████▎ | 380/520 [23:36<08:25, 3.61s/it] {'loss': 1.2092, 'grad_norm': 0.0005167397362405229, 'learning_rate': 0.035721239031346066, 'epoch': 0.73} + 73%|███████▎ | 380/520 [23:36<08:25, 3.61s/it] 73%|███████▎ | 381/520 [23:40<08:22, 3.61s/it] {'loss': 1.2083, 'grad_norm': 0.000498214151697933, 'learning_rate': 0.03524499095177297, 'epoch': 0.73} + 73%|███████▎ | 381/520 [23:40<08:22, 3.61s/it] 73%|███████▎ | 382/520 [23:44<08:20, 3.63s/it] {'loss': 1.1825, 'grad_norm': 0.0004795596552734144, 'learning_rate': 0.03477125887218792, 'epoch': 0.73} + 73%|███████▎ | 382/520 [23:44<08:20, 3.63s/it] 74%|███████▎ | 383/520 [23:47<08:15, 3.62s/it] {'loss': 1.0501, 'grad_norm': 0.000594674750598415, 'learning_rate': 0.03430006119904196, 'epoch': 0.74} + 74%|███████▎ | 383/520 [23:47<08:15, 3.62s/it] 74%|███████▍ | 384/520 [23:51<08:11, 3.62s/it] {'loss': 1.2025, 'grad_norm': 0.0004952003545022782, 'learning_rate': 0.033831416240314084, 'epoch': 0.74} + 74%|███████▍ | 384/520 [23:51<08:11, 3.62s/it] 74%|███████▍ | 385/520 [23:54<08:07, 3.61s/it] {'loss': 1.1917, 'grad_norm': 0.0004922881306989438, 'learning_rate': 0.03336534220479961, 'epoch': 0.74} + 74%|███████▍ | 385/520 [23:54<08:07, 3.61s/it] 74%|███████▍ | 386/520 [23:58<08:04, 3.62s/it] {'loss': 1.1439, 'grad_norm': 0.00046878561007816165, 'learning_rate': 0.032901857201403005, 'epoch': 0.74} + 74%|███████▍ | 386/520 [23:58<08:04, 3.62s/it] 74%|███████▍ | 387/520 [24:02<08:02, 3.63s/it] {'loss': 1.2281, 'grad_norm': 0.0005151703229571998, 'learning_rate': 0.032440979238433976, 'epoch': 0.74} + 74%|███████▍ | 387/520 [24:02<08:02, 3.63s/it] 75%|███████▍ | 388/520 [24:05<07:58, 3.63s/it] {'loss': 1.1039, 'grad_norm': 0.000507174364909528, 'learning_rate': 0.03198272622290804, 'epoch': 0.75} + 75%|███████▍ | 388/520 [24:05<07:58, 3.63s/it] 75%|███████▍ | 389/520 [24:09<07:54, 3.62s/it] {'loss': 1.1511, 'grad_norm': 0.0006945563090771835, 'learning_rate': 0.03152711595985065, 'epoch': 0.75} + 75%|███████▍ | 389/520 [24:09<07:54, 3.62s/it] 75%|███████▌ | 390/520 [24:13<07:50, 3.62s/it] {'loss': 1.2174, 'grad_norm': 0.0005232982980480297, 'learning_rate': 0.031074166151605298, 'epoch': 0.75} + 75%|███████▌ | 390/520 [24:13<07:50, 3.62s/it] 75%|███████▌ | 391/520 [24:16<07:48, 3.63s/it] {'loss': 1.2759, 'grad_norm': 0.000553653826371492, 'learning_rate': 0.030623894397145836, 'epoch': 0.75} + 75%|███████▌ | 391/520 [24:16<07:48, 3.63s/it] 75%|███████▌ | 392/520 [24:20<07:44, 3.63s/it] {'loss': 1.1043, 'grad_norm': 0.0005289176919885457, 'learning_rate': 0.03017631819139273, 'epoch': 0.75} + 75%|███████▌ | 392/520 [24:20<07:44, 3.63s/it] 76%|███████▌ | 393/520 [24:23<07:41, 3.64s/it] {'loss': 1.0923, 'grad_norm': 0.00042669144508010644, 'learning_rate': 0.029731454924533086, 'epoch': 0.76} + 76%|███████▌ | 393/520 [24:23<07:41, 3.64s/it] 76%|███████▌ | 394/520 [24:27<07:36, 3.62s/it] {'loss': 1.1757, 'grad_norm': 0.0005678170112069761, 'learning_rate': 0.029289321881345254, 'epoch': 0.76} + 76%|███████▌ | 394/520 [24:27<07:36, 3.62s/it] 76%|███████▌ | 395/520 [24:31<07:33, 3.63s/it] {'loss': 1.1394, 'grad_norm': 0.0005783527167573125, 'learning_rate': 0.028849936240527008, 'epoch': 0.76} + 76%|███████▌ | 395/520 [24:31<07:33, 3.63s/it] 76%|███████▌ | 396/520 [24:34<07:28, 3.62s/it] {'loss': 1.2164, 'grad_norm': 0.0005881343419801509, 'learning_rate': 0.028413315074028157, 'epoch': 0.76} + 76%|███████▌ | 396/520 [24:34<07:28, 3.62s/it] 76%|███████▋ | 397/520 [24:38<07:25, 3.62s/it] {'loss': 1.1907, 'grad_norm': 0.0004960518713660932, 'learning_rate': 0.027979475346387363, 'epoch': 0.76} + 76%|███████▋ | 397/520 [24:38<07:25, 3.62s/it] 77%|███████▋ | 398/520 [24:42<07:21, 3.62s/it] {'loss': 1.1876, 'grad_norm': 0.0005527214549905824, 'learning_rate': 0.027548433914072735, 'epoch': 0.77} + 77%|███████▋ | 398/520 [24:42<07:21, 3.62s/it] 77%|███████▋ | 399/520 [24:45<07:19, 3.63s/it] {'loss': 1.1239, 'grad_norm': 0.0005094279685994074, 'learning_rate': 0.027120207524827168, 'epoch': 0.77} + 77%|███████▋ | 399/520 [24:45<07:19, 3.63s/it] 77%|███████▋ | 400/520 [24:49<07:16, 3.64s/it] {'loss': 1.156, 'grad_norm': 0.0005094021944302383, 'learning_rate': 0.02669481281701739, 'epoch': 0.77} + 77%|███████▋ | 400/520 [24:49<07:16, 3.64s/it] 77%|███████▋ | 401/520 [24:52<07:12, 3.63s/it] {'loss': 1.0276, 'grad_norm': 0.000586337827310684, 'learning_rate': 0.026272266318987603, 'epoch': 0.77} + 77%|███████▋ | 401/520 [24:52<07:12, 3.63s/it] 77%|███████▋ | 402/520 [24:56<07:07, 3.62s/it] {'loss': 1.157, 'grad_norm': 0.0005331996571350047, 'learning_rate': 0.02585258444841733, 'epoch': 0.77} + 77%|███████▋ | 402/520 [24:56<07:07, 3.62s/it] 78%|███████▊ | 403/520 [25:00<07:04, 3.62s/it] {'loss': 1.1776, 'grad_norm': 0.0005596814024300232, 'learning_rate': 0.025435783511683442, 'epoch': 0.78} + 78%|███████▊ | 403/520 [25:00<07:04, 3.62s/it] 78%|███████▊ | 404/520 [25:03<06:59, 3.61s/it] {'loss': 1.0898, 'grad_norm': 0.000603769145946243, 'learning_rate': 0.02502187970322657, 'epoch': 0.78} + 78%|███████▊ | 404/520 [25:03<06:59, 3.61s/it] 78%|███████▊ | 405/520 [25:07<06:54, 3.60s/it] {'loss': 1.1376, 'grad_norm': 0.0005016888737076647, 'learning_rate': 0.02461088910492202, 'epoch': 0.78} + 78%|███████▊ | 405/520 [25:07<06:54, 3.60s/it] 78%|███████▊ | 406/520 [25:10<06:51, 3.61s/it] {'loss': 1.0603, 'grad_norm': 0.0007465857612963497, 'learning_rate': 0.02420282768545469, 'epoch': 0.78} + 78%|███████▊ | 406/520 [25:11<06:51, 3.61s/it] 78%|███████▊ | 407/520 [25:14<06:47, 3.61s/it] {'loss': 1.2536, 'grad_norm': 0.0005853084193939235, 'learning_rate': 0.02379771129969892, 'epoch': 0.78} + 78%|███████▊ | 407/520 [25:14<06:47, 3.61s/it] 78%|███████▊ | 408/520 [25:18<06:43, 3.60s/it] {'loss': 1.1715, 'grad_norm': 0.0007159947099069969, 'learning_rate': 0.023395555688102213, 'epoch': 0.78} + 78%|███████▊ | 408/520 [25:18<06:43, 3.60s/it] 79%|███████▊ | 409/520 [25:21<06:40, 3.60s/it] {'loss': 1.2836, 'grad_norm': 0.0006393228111674666, 'learning_rate': 0.02299637647607372, 'epoch': 0.79} + 79%|███████▊ | 409/520 [25:21<06:40, 3.60s/it] 79%|███████▉ | 410/520 [25:25<06:37, 3.61s/it] {'loss': 1.0291, 'grad_norm': 0.0005256982204234877, 'learning_rate': 0.022600189173377264, 'epoch': 0.79} + 79%|███████▉ | 410/520 [25:25<06:37, 3.61s/it] 79%|███████▉ | 411/520 [25:29<06:33, 3.61s/it] {'loss': 1.2665, 'grad_norm': 0.0005607350746161208, 'learning_rate': 0.022207009173528525, 'epoch': 0.79} + 79%|███████▉ | 411/520 [25:29<06:33, 3.61s/it] 79%|███████▉ | 412/520 [25:32<06:29, 3.61s/it] {'loss': 1.1767, 'grad_norm': 0.000509797116765475, 'learning_rate': 0.02181685175319702, 'epoch': 0.79} + 79%|███████▉ | 412/520 [25:32<06:29, 3.61s/it] 79%|███████▉ | 413/520 [25:36<06:27, 3.62s/it] {'loss': 1.1522, 'grad_norm': 0.0004973376264270996, 'learning_rate': 0.021429732071612653, 'epoch': 0.79} + 79%|███████▉ | 413/520 [25:36<06:27, 3.62s/it] 80%|███████▉ | 414/520 [25:39<06:23, 3.62s/it] {'loss': 0.9657, 'grad_norm': 0.00043392134658083546, 'learning_rate': 0.02104566516997647, 'epoch': 0.8} + 80%|███████▉ | 414/520 [25:39<06:23, 3.62s/it] 80%|███████▉ | 415/520 [25:43<06:18, 3.61s/it] {'loss': 1.1597, 'grad_norm': 0.00050423322967236, 'learning_rate': 0.020664665970876496, 'epoch': 0.8} + 80%|███████▉ | 415/520 [25:43<06:18, 3.61s/it] 80%|████████ | 416/520 [25:47<06:16, 3.62s/it] {'loss': 1.0637, 'grad_norm': 0.0006142961328958643, 'learning_rate': 0.020286749277707784, 'epoch': 0.8} + 80%|████████ | 416/520 [25:47<06:16, 3.62s/it] 80%|████████ | 417/520 [25:50<06:12, 3.62s/it] {'loss': 1.2253, 'grad_norm': 0.0005284382470212891, 'learning_rate': 0.019911929774097215, 'epoch': 0.8} + 80%|████████ | 417/520 [25:50<06:12, 3.62s/it] 80%|████████ | 418/520 [25:54<06:09, 3.62s/it] {'loss': 1.2189, 'grad_norm': 0.0005515391046588563, 'learning_rate': 0.019540222023333165, 'epoch': 0.8} + 80%|████████ | 418/520 [25:54<06:09, 3.62s/it] 81%|████████ | 419/520 [25:57<06:05, 3.62s/it] {'loss': 1.2143, 'grad_norm': 0.0005613843376861208, 'learning_rate': 0.01917164046779948, 'epoch': 0.81} + 81%|████████ | 419/520 [25:57<06:05, 3.62s/it] 81%|████████ | 420/520 [26:01<06:01, 3.61s/it] {'loss': 1.1056, 'grad_norm': 0.0005399271511080432, 'learning_rate': 0.018806199428414352, 'epoch': 0.81} + 81%|████████ | 420/520 [26:01<06:01, 3.61s/it] 81%|████████ | 421/520 [26:05<05:58, 3.62s/it] {'loss': 1.0432, 'grad_norm': 0.0005797385449147667, 'learning_rate': 0.018443913104073985, 'epoch': 0.81} + 81%|████████ | 421/520 [26:05<05:58, 3.62s/it] 81%|████████ | 422/520 [26:08<05:53, 3.61s/it] {'loss': 1.1645, 'grad_norm': 0.0005590268389829667, 'learning_rate': 0.01808479557110081, 'epoch': 0.81} + 81%|████████ | 422/520 [26:08<05:53, 3.61s/it] 81%|████████▏ | 423/520 [26:12<05:50, 3.61s/it] {'loss': 1.1307, 'grad_norm': 0.0005847926245121474, 'learning_rate': 0.017728860782696667, 'epoch': 0.81} + 81%|████████▏ | 423/520 [26:12<05:50, 3.61s/it] 82%|████████▏ | 424/520 [26:16<05:46, 3.61s/it] {'loss': 1.2343, 'grad_norm': 0.0005110157210731019, 'learning_rate': 0.017376122568400532, 'epoch': 0.82} + 82%|████████▏ | 424/520 [26:16<05:46, 3.61s/it] 82%|████████▏ | 425/520 [26:19<05:43, 3.61s/it] {'loss': 1.1479, 'grad_norm': 0.0004992527913569714, 'learning_rate': 0.017026594633551252, 'epoch': 0.82} + 82%|████████▏ | 425/520 [26:19<05:43, 3.61s/it] 82%|████████▏ | 426/520 [26:23<05:38, 3.60s/it] {'loss': 1.1811, 'grad_norm': 0.0007250908647037563, 'learning_rate': 0.01668029055875512, 'epoch': 0.82} + 82%|████████▏ | 426/520 [26:23<05:38, 3.60s/it] 82%|████████▏ | 427/520 [26:26<05:33, 3.59s/it] {'loss': 1.0813, 'grad_norm': 0.0004928736309957419, 'learning_rate': 0.016337223799358026, 'epoch': 0.82} + 82%|████████▏ | 427/520 [26:26<05:33, 3.59s/it] 82%|████████▏ | 428/520 [26:30<05:31, 3.60s/it] {'loss': 1.0734, 'grad_norm': 0.0005566529382342531, 'learning_rate': 0.01599740768492286, 'epoch': 0.82} + 82%|████████▏ | 428/520 [26:30<05:31, 3.60s/it] 82%|████████▎ | 429/520 [26:34<05:28, 3.61s/it] {'loss': 1.1705, 'grad_norm': 0.0006292754772248582, 'learning_rate': 0.015660855418711452, 'epoch': 0.82} + 82%|████████▎ | 429/520 [26:34<05:28, 3.61s/it]Token indices sequence length is longer than the specified maximum sequence length for this model (2076 > 2048). Running this sequence through the model will result in indexing errors + 83%|████████▎ | 430/520 [26:37<05:23, 3.60s/it] {'loss': 1.1727, 'grad_norm': 0.0004970095820831776, 'learning_rate': 0.015327580077171589, 'epoch': 0.83} + 83%|████████▎ | 430/520 [26:37<05:23, 3.60s/it] 83%|████████▎ | 431/520 [26:41<05:20, 3.60s/it] {'loss': 1.1236, 'grad_norm': 0.0005884021936275156, 'learning_rate': 0.014997594609429088, 'epoch': 0.83} + 83%|████████▎ | 431/520 [26:41<05:20, 3.60s/it] 83%|████████▎ | 432/520 [26:44<05:17, 3.61s/it] {'loss': 1.0783, 'grad_norm': 0.0005522893187178254, 'learning_rate': 0.01467091183678444, 'epoch': 0.83} + 83%|████████▎ | 432/520 [26:44<05:17, 3.61s/it] 83%|████████▎ | 433/520 [26:48<05:13, 3.60s/it] {'loss': 1.2104, 'grad_norm': 0.0006008322505035165, 'learning_rate': 0.014347544452214867, 'epoch': 0.83} + 83%|████████▎ | 433/520 [26:48<05:13, 3.60s/it] 83%|████████▎ | 434/520 [26:52<05:09, 3.60s/it] {'loss': 0.964, 'grad_norm': 0.0005300390752140238, 'learning_rate': 0.014027505019880971, 'epoch': 0.83} + 83%|████████▎ | 434/520 [26:52<05:09, 3.60s/it] 84%|████████▎ | 435/520 [26:55<05:05, 3.59s/it] {'loss': 1.2424, 'grad_norm': 0.0005926359239645679, 'learning_rate': 0.013710805974638696, 'epoch': 0.84} + 84%|████████▎ | 435/520 [26:55<05:05, 3.59s/it] 84%|████████▍ | 436/520 [26:59<05:02, 3.61s/it] {'loss': 1.0534, 'grad_norm': 0.0005357955492531617, 'learning_rate': 0.01339745962155613, 'epoch': 0.84} + 84%|████████▍ | 436/520 [26:59<05:02, 3.61s/it] 84%|████████▍ | 437/520 [27:02<04:59, 3.61s/it] {'loss': 1.2626, 'grad_norm': 0.0005194059193060794, 'learning_rate': 0.01308747813543536, 'epoch': 0.84} + 84%|████████▍ | 437/520 [27:02<04:59, 3.61s/it] 84%|████████▍ | 438/520 [27:06<04:55, 3.60s/it] {'loss': 1.089, 'grad_norm': 0.0005351520224999149, 'learning_rate': 0.012780873560339467, 'epoch': 0.84} + 84%|████████▍ | 438/520 [27:06<04:55, 3.60s/it] 84%|████████▍ | 439/520 [27:10<04:51, 3.60s/it] {'loss': 1.1086, 'grad_norm': 0.0004213823101852855, 'learning_rate': 0.012477657809124632, 'epoch': 0.84} + 84%|████████▍ | 439/520 [27:10<04:51, 3.60s/it] 85%|████████▍ | 440/520 [27:13<04:48, 3.60s/it] {'loss': 1.1218, 'grad_norm': 0.0005906096088814127, 'learning_rate': 0.012177842662977134, 'epoch': 0.85} + 85%|████████▍ | 440/520 [27:13<04:48, 3.60s/it] 85%|████████▍ | 441/520 [27:17<04:46, 3.62s/it] {'loss': 1.121, 'grad_norm': 0.0006639014317715373, 'learning_rate': 0.01188143977095576, 'epoch': 0.85} + 85%|████████▍ | 441/520 [27:17<04:46, 3.62s/it] 85%|████████▌ | 442/520 [27:20<04:42, 3.63s/it] {'loss': 1.1845, 'grad_norm': 0.0005912978304724034, 'learning_rate': 0.011588460649539035, 'epoch': 0.85} + 85%|████████▌ | 442/520 [27:20<04:42, 3.63s/it] 85%|████████▌ | 443/520 [27:24<04:39, 3.63s/it] {'loss': 1.1929, 'grad_norm': 0.0005272547817956476, 'learning_rate': 0.011298916682177829, 'epoch': 0.85} + 85%|████████▌ | 443/520 [27:24<04:39, 3.63s/it] 85%|████████▌ | 444/520 [27:28<04:36, 3.63s/it] {'loss': 1.1603, 'grad_norm': 0.0004645821558190926, 'learning_rate': 0.011012819118853146, 'epoch': 0.85} + 85%|████████▌ | 444/520 [27:28<04:36, 3.63s/it] 86%|████████▌ | 445/520 [27:31<04:31, 3.62s/it] {'loss': 1.0877, 'grad_norm': 0.0005063787972700343, 'learning_rate': 0.01073017907563887, 'epoch': 0.86} + 86%|████████▌ | 445/520 [27:31<04:31, 3.62s/it] 86%|████████▌ | 446/520 [27:35<04:27, 3.61s/it] {'loss': 1.1986, 'grad_norm': 0.0004745940833716154, 'learning_rate': 0.010451007534269908, 'epoch': 0.86} + 86%|████████▌ | 446/520 [27:35<04:27, 3.61s/it] 86%|████████▌ | 447/520 [27:39<04:24, 3.63s/it] {'loss': 1.1573, 'grad_norm': 0.0005058723563759397, 'learning_rate': 0.010175315341715598, 'epoch': 0.86} + 86%|████████▌ | 447/520 [27:39<04:24, 3.63s/it] 86%|████████▌ | 448/520 [27:42<04:20, 3.61s/it] {'loss': 1.1614, 'grad_norm': 0.0005954143692927593, 'learning_rate': 0.009903113209758098, 'epoch': 0.86} + 86%|████████▌ | 448/520 [27:42<04:20, 3.61s/it] 86%|████████▋ | 449/520 [27:46<04:16, 3.62s/it] {'loss': 1.1595, 'grad_norm': 0.0005135437054894886, 'learning_rate': 0.009634411714576352, 'epoch': 0.86} + 86%|████████▋ | 449/520 [27:46<04:16, 3.62s/it] 87%|████████▋ | 450/520 [27:49<04:13, 3.62s/it] {'loss': 1.1808, 'grad_norm': 0.00052131024155406, 'learning_rate': 0.009369221296335007, 'epoch': 0.87} + 87%|████████▋ | 450/520 [27:49<04:13, 3.62s/it] 87%|████████▋ | 451/520 [27:53<04:09, 3.61s/it] {'loss': 1.184, 'grad_norm': 0.0005362281322720759, 'learning_rate': 0.009107552258778906, 'epoch': 0.87} + 87%|████████▋ | 451/520 [27:53<04:09, 3.61s/it] 87%|████████▋ | 452/520 [27:57<04:06, 3.62s/it] {'loss': 1.2038, 'grad_norm': 0.0005200816474671308, 'learning_rate': 0.008849414768832687, 'epoch': 0.87} + 87%|████████▋ | 452/520 [27:57<04:06, 3.62s/it] 87%|████████▋ | 453/520 [28:00<04:02, 3.62s/it] {'loss': 1.1803, 'grad_norm': 0.0005189497979105308, 'learning_rate': 0.008594818856205699, 'epoch': 0.87} + 87%|████████▋ | 453/520 [28:00<04:02, 3.62s/it] 87%|████████▋ | 454/520 [28:04<03:59, 3.62s/it] {'loss': 1.0925, 'grad_norm': 0.0005289840761311818, 'learning_rate': 0.00834377441300238, 'epoch': 0.87} + 87%|████████▋ | 454/520 [28:04<03:59, 3.62s/it] 88%|████████▊ | 455/520 [28:07<03:54, 3.61s/it] {'loss': 1.2322, 'grad_norm': 0.0005232777246906555, 'learning_rate': 0.008096291193337934, 'epoch': 0.88} + 88%|████████▊ | 455/520 [28:07<03:54, 3.61s/it] 88%|████████▊ | 456/520 [28:11<03:51, 3.61s/it] {'loss': 1.167, 'grad_norm': 0.0005715566592052937, 'learning_rate': 0.007852378812959226, 'epoch': 0.88} + 88%|████████▊ | 456/520 [28:11<03:51, 3.61s/it] 88%|████████▊ | 457/520 [28:15<03:47, 3.62s/it] {'loss': 1.0666, 'grad_norm': 0.00044717240729792495, 'learning_rate': 0.007612046748871327, 'epoch': 0.88} + 88%|████████▊ | 457/520 [28:15<03:47, 3.62s/it] 88%|████████▊ | 458/520 [28:18<03:46, 3.66s/it] {'loss': 1.2827, 'grad_norm': 0.0005745425440950007, 'learning_rate': 0.007375304338969136, 'epoch': 0.88} + 88%|████████▊ | 458/520 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100%|██████████| 520/520 [32:05<00:00, 3.90s/it] 100%|██████████| 520/520 [32:05<00:00, 3.70s/it] +[2025-10-18 05:54:43,616] [INFO] [launch.py:348:main] Process 987047 exits successfully. +[2025-10-18 05:54:43,617] [INFO] [launch.py:348:main] Process 987042 exits successfully. +[2025-10-18 05:54:43,617] [INFO] [launch.py:348:main] Process 987045 exits successfully. +[2025-10-18 05:54:43,618] [INFO] [launch.py:348:main] Process 987048 exits successfully. +[2025-10-18 05:54:44,619] [INFO] [launch.py:348:main] Process 987044 exits successfully. +[2025-10-18 05:54:44,620] [INFO] [launch.py:348:main] Process 987046 exits successfully. +[2025-10-18 05:54:44,620] [INFO] [launch.py:348:main] Process 987043 exits successfully. +[2025-10-18 05:54:47,624] [INFO] [launch.py:348:main] Process 987041 exits successfully. +==== EXPERIMENT COMPLETED: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.7_2e-1_connector-9.0_2.7_2e-1_ablation_20251018_052106.log +Timestamp: 2025-10-18 05:54:50 +===================================== diff --git a/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation_20251018_055450.log b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation_20251018_055450.log new file mode 100644 index 0000000000000000000000000000000000000000..94030a2c1bb2e9bef9a3395c1295b7af5cbd6507 --- /dev/null +++ b/logs_oct17/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation_20251018_055450.log @@ -0,0 +1,2082 @@ +==== STARTING EXPERIMENT: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation ==== +Log File: qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation_20251018_055450.log +Timestamp: 2025-10-18 05:54:50 +===================================== +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:54:52,748] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:54:55,722] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only. +[2025-10-18 05:54:55,724] [INFO] [runner.py:568:main] cmd = /opt/conda/envs/tinyllava/bin/python3.10 -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=29501 --enable_each_rank_log=None tinyllava/train/train.py --deepspeed ./scripts/zero3.json --data_path /s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json --image_folder /s3-code/ywang29/datasets/tinyllava --is_multimodal True --conv_version qwen2_base --model_name_or_path Qwen/Qwen2.5-0.5B --vision_tower google/siglip-so400m-patch14-384 --vision_tower2 --connector_type mlp2x_gelu --mm_vision_select_layer -2 --image_aspect_ratio square --attn_implementation flash_attention_2 --bf16 True --training_recipe common --tune_type_llm full --tune_type_vision_tower frozen --tune_vision_tower_from_layer 0 --tune_type_connector full --group_by_modality_length True --pretrained_model_path /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain --output_dir /nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation --num_train_epochs 1 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --evaluation_strategy no --learning_rate 2e-1 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 False --model_max_length 2048 --gradient_checkpointing True --dataloader_num_workers 8 --lazy_preprocess True --report_to tensorboard --tokenizer_use_fast False --run_name tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune --subnet_mode_text both --subnet_type_text None --mask_type_text soft --init_mean_text 9.0 --temperature_attn_text 2.9 --temperature_mlp_text 2.9 --backward_type_text normal --masked_layers_text all --subnet_mode_vision both --subnet_type_vision None --mask_type_vision soft --init_mean_vision 9.0 --temperature_attn_vision 2.9 --temperature_mlp_vision 2.9 --backward_type_vision normal --masked_layers_vision all --subnet_type_connector global --mask_type_connector soft --init_mean_connector 9.0 --temperature_connector 2.9 --backward_type_connector normal --mm_projector_lr 2e-1 --seed 42 --mask_model llm-connector --save_strategy steps --save_steps 50000 --save_total_limit 1 --train_data_ratio 0.1 +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:54:58,289] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:54:59,350] [INFO] [launch.py:138:main] 0 NCCL_VERSION=2.21.5 +[2025-10-18 05:54:59,350] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=eth +[2025-10-18 05:54:59,350] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]} +[2025-10-18 05:54:59,350] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=8, node_rank=0 +[2025-10-18 05:54:59,350] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}) +[2025-10-18 05:54:59,350] [INFO] [launch.py:163:main] dist_world_size=8 +[2025-10-18 05:54:59,351] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 +[2025-10-18 05:54:59,353] [INFO] [launch.py:253:main] process 1008836 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=0', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,355] [INFO] [launch.py:253:main] process 1008837 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=1', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,357] [INFO] [launch.py:253:main] process 1008838 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=2', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,359] [INFO] [launch.py:253:main] process 1008839 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=3', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,360] [INFO] [launch.py:253:main] process 1008840 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=4', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,362] [INFO] [launch.py:253:main] process 1008841 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=5', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,364] [INFO] [launch.py:253:main] process 1008842 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=6', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +[2025-10-18 05:54:59,366] [INFO] [launch.py:253:main] process 1008843 spawned with command: ['/opt/conda/envs/tinyllava/bin/python3.10', '-u', 'tinyllava/train/train.py', '--local_rank=7', '--deepspeed', './scripts/zero3.json', '--data_path', '/s3-code/ywang29/datasets/tinyllava/text_files/llava_v1_5_mix665k.json', '--image_folder', '/s3-code/ywang29/datasets/tinyllava', '--is_multimodal', 'True', '--conv_version', 'qwen2_base', '--model_name_or_path', 'Qwen/Qwen2.5-0.5B', '--vision_tower', 'google/siglip-so400m-patch14-384', '--vision_tower2', '', '--connector_type', 'mlp2x_gelu', '--mm_vision_select_layer', '-2', '--image_aspect_ratio', 'square', '--attn_implementation', 'flash_attention_2', '--bf16', 'True', '--training_recipe', 'common', '--tune_type_llm', 'full', '--tune_type_vision_tower', 'frozen', '--tune_vision_tower_from_layer', '0', '--tune_type_connector', 'full', '--group_by_modality_length', 'True', '--pretrained_model_path', '/nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain', '--output_dir', '/nfs/ywang29/TinyLLaVA/checkpoints/qwen2.5-0_5b_base_masktune_42_llm-connector_text-9.0_2.9_2e-1_connector-9.0_2.9_2e-1_ablation', '--num_train_epochs', '1', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--evaluation_strategy', 'no', '--learning_rate', '2e-1', '--weight_decay', '0.', '--warmup_ratio', '0.03', '--lr_scheduler_type', 'cosine', '--logging_steps', '1', '--tf32', 'False', '--model_max_length', '2048', '--gradient_checkpointing', 'True', '--dataloader_num_workers', '8', '--lazy_preprocess', 'True', '--report_to', 'tensorboard', '--tokenizer_use_fast', 'False', '--run_name', 'tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-qwen2.5-0_5b_base-masktune', '--subnet_mode_text', 'both', '--subnet_type_text', 'None', '--mask_type_text', 'soft', '--init_mean_text', '9.0', '--temperature_attn_text', '2.9', '--temperature_mlp_text', '2.9', '--backward_type_text', 'normal', '--masked_layers_text', 'all', '--subnet_mode_vision', 'both', '--subnet_type_vision', 'None', '--mask_type_vision', 'soft', '--init_mean_vision', '9.0', '--temperature_attn_vision', '2.9', '--temperature_mlp_vision', '2.9', '--backward_type_vision', 'normal', '--masked_layers_vision', 'all', '--subnet_type_connector', 'global', '--mask_type_connector', 'soft', '--init_mean_connector', '9.0', '--temperature_connector', '2.9', '--backward_type_connector', 'normal', '--mm_projector_lr', '2e-1', '--seed', '42', '--mask_model', 'llm-connector', '--save_strategy', 'steps', '--save_steps', '50000', '--save_total_limit', '1', '--train_data_ratio', '0.1'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/cuda/__init__.py:51: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you. + import pynvml # type: ignore[import] +[2025-10-18 05:55:06,173] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,460] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,502] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,548] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,563] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,563] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,582] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:06,582] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl +[2025-10-18 05:55:06,632] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,639] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect) +[2025-10-18 05:55:06,886] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:06,928] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:06,973] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:06,987] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:06,989] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:07,053] [INFO] [comm.py:637:init_distributed] cdb=None +[2025-10-18 05:55:07,061] [INFO] [comm.py:637:init_distributed] cdb=None +Apply masks for the following modules: ['llm', 'connector'] +{'llm': {'model_name_or_path': 'Qwen/Qwen2.5-0.5B', 'cache_dir': None, 'attn_implementation': 'flash_attention_2', 'subnet_mode': 'both', 'subnet_type': 'None', 'sparsity_attn': None, 'sparsity_mlp': None, 'threshold_attn': None, 'threshold_mlp': None, 'temperature_attn': 2.9, 'temperature_mlp': 2.9, 'masked_layers': 'all', 'mask_type': 'soft', 'backward_type': 'normal'}, 'vision_tower': {'model_name_or_path': 'google/siglip-so400m-patch14-384'}, 'connector': {'connector_type': 'mlp2x_gelu', 'subnet_type': 'global', 'threshold': None, 'sparsity': None, 'temperature': 2.9, 'mask_type': 'soft', 'backward_type': 'normal'}} +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +Apply masks for the following modules: ['llm', 'connector'] +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/huggingface_hub/file_download.py:945: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. + warnings.warn( +TinyLlavaConfig { + "backward_type_connector": "normal", + "cache_dir": null, + "connector_type": "mlp2x_gelu", + "hidden_size": 896, + "ignore_index": -100, + "image_aspect_ratio": "square", + "image_token_index": -200, + "llm_model_name_or_path": "Qwen/Qwen2.5-0.5B", + "mask_model": [ + "llm", + "connector" + ], + "mask_type_connector": "soft", + "model_type": "tinyllava", + "num_queries": 128, + "num_resampler_layers": 3, + "pad_token": null, + "resampler_hidden_size": 768, + "sparsity_connector": null, + "subnet_type_connector": "global", + "temperature_connector": 2.9, + "text_config": { + "_name_or_path": "Qwen/Qwen2.5-0.5B", + "architectures": [ + "Qwen2ForCausalLM" + ], + "backward_type": "normal", + "bos_token_id": 151643, + "eos_token_id": 151643, + "hidden_size": 896, + "intermediate_size": 4864, + "mask_type": "soft", + "masked_layers": "all", + "max_position_embeddings": 32768, + "max_window_layers": 24, + "model_type": "qwen2", + "num_attention_heads": 14, + "num_hidden_layers": 24, + "num_key_value_heads": 2, + "rope_theta": 1000000.0, + "sliding_window": 32768, + "subnet_mode": "both", + "subnet_type": "None", + "temperature_attn": 2.9, + "temperature_mlp": 2.9, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "use_mrope": false, + "use_sliding_window": false, + "vocab_size": 151936 + }, + "threshold_connector": null, + "tokenizer_model_max_length": 2048, + "tokenizer_name_or_path": "Qwen/Qwen2.5-0.5B", + "tokenizer_padding_side": "right", + "tokenizer_use_fast": false, + "transformers_version": "4.40.1", + "tune_type_connector": "frozen", + "tune_type_llm": "frozen", + "tune_type_vision_tower": "frozen", + "tune_vision_tower_from_layer": -1, + "use_cache": false, + "vision_config": { + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_name_or_path": "google/siglip-so400m-patch14-384", + "model_name_or_path2": "", + "model_type": "siglip_vision_model", + "num_attention_heads": 16, + "num_hidden_layers": 27, + "patch_size": 14 + }, + "vision_feature_layer": -2, + "vision_feature_select_strategy": "patch", + "vision_hidden_size": 1152, + "vision_model_name_or_path": "google/siglip-so400m-patch14-384", + "vision_model_name_or_path2": "", + "vocab_size": 151936 +} + +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to eth +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO Bootstrap : Using eth0:10.200.208.58<0> +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so) +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO NET/Plugin: Using internal network plugin. +ywang29-vrdb-test1-worker-0:1008836:1008836 [0] NCCL INFO cudaDriverVersion 12040 +NCCL version 2.21.5+cuda12.1 +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() + return self.fget.__get__(instance, owner)() +Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained. +/opt/conda/envs/tinyllava/lib/python3.10/site-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. 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7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Trees [0] -1/-1/-1->7->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1010408 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1010410 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1010409 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read 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[6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO 24 coll 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[4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008841:1010428 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so 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rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 901c0 commId 0x8e5c449acd6816cf - Init COMPLETE +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:1008836:1010406 [0] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:1008838:1010407 [2] NCCL INFO TUNER/Plugin: Plugin load returned 11 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so +ywang29-vrdb-test1-worker-0:1008842:1010429 [6] NCCL INFO ncclCommInitRank comm 0x556ab36d6280 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId a01c0 commId 0x8e5c449acd6816cf - Init COMPLETE +ywang29-vrdb-test1-worker-0:1008837:1010415 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin. 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'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +Some weights of Qwen2ForCausalLM were not initialized from the model checkpoint at /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model and are newly initialized: ['model.layers.0.mlp.down_proj.scores', 'model.layers.0.mlp.gate_proj.scores', 'model.layers.0.mlp.up_proj.scores', 'model.layers.0.self_attn.k_proj.scores', 'model.layers.0.self_attn.o_proj.scores', 'model.layers.0.self_attn.q_proj.scores', 'model.layers.0.self_attn.v_proj.scores', 'model.layers.1.mlp.down_proj.scores', 'model.layers.1.mlp.gate_proj.scores', 'model.layers.1.mlp.up_proj.scores', 'model.layers.1.self_attn.k_proj.scores', 'model.layers.1.self_attn.o_proj.scores', 'model.layers.1.self_attn.q_proj.scores', 'model.layers.1.self_attn.v_proj.scores', 'model.layers.10.mlp.down_proj.scores', 'model.layers.10.mlp.gate_proj.scores', 'model.layers.10.mlp.up_proj.scores', 'model.layers.10.self_attn.k_proj.scores', 'model.layers.10.self_attn.o_proj.scores', 'model.layers.10.self_attn.q_proj.scores', 'model.layers.10.self_attn.v_proj.scores', 'model.layers.11.mlp.down_proj.scores', 'model.layers.11.mlp.gate_proj.scores', 'model.layers.11.mlp.up_proj.scores', 'model.layers.11.self_attn.k_proj.scores', 'model.layers.11.self_attn.o_proj.scores', 'model.layers.11.self_attn.q_proj.scores', 'model.layers.11.self_attn.v_proj.scores', 'model.layers.12.mlp.down_proj.scores', 'model.layers.12.mlp.gate_proj.scores', 'model.layers.12.mlp.up_proj.scores', 'model.layers.12.self_attn.k_proj.scores', 'model.layers.12.self_attn.o_proj.scores', 'model.layers.12.self_attn.q_proj.scores', 'model.layers.12.self_attn.v_proj.scores', 'model.layers.13.mlp.down_proj.scores', 'model.layers.13.mlp.gate_proj.scores', 'model.layers.13.mlp.up_proj.scores', 'model.layers.13.self_attn.k_proj.scores', 'model.layers.13.self_attn.o_proj.scores', 'model.layers.13.self_attn.q_proj.scores', 'model.layers.13.self_attn.v_proj.scores', 'model.layers.14.mlp.down_proj.scores', 'model.layers.14.mlp.gate_proj.scores', 'model.layers.14.mlp.up_proj.scores', 'model.layers.14.self_attn.k_proj.scores', 'model.layers.14.self_attn.o_proj.scores', 'model.layers.14.self_attn.q_proj.scores', 'model.layers.14.self_attn.v_proj.scores', 'model.layers.15.mlp.down_proj.scores', 'model.layers.15.mlp.gate_proj.scores', 'model.layers.15.mlp.up_proj.scores', 'model.layers.15.self_attn.k_proj.scores', 'model.layers.15.self_attn.o_proj.scores', 'model.layers.15.self_attn.q_proj.scores', 'model.layers.15.self_attn.v_proj.scores', 'model.layers.16.mlp.down_proj.scores', 'model.layers.16.mlp.gate_proj.scores', 'model.layers.16.mlp.up_proj.scores', 'model.layers.16.self_attn.k_proj.scores', 'model.layers.16.self_attn.o_proj.scores', 'model.layers.16.self_attn.q_proj.scores', 'model.layers.16.self_attn.v_proj.scores', 'model.layers.17.mlp.down_proj.scores', 'model.layers.17.mlp.gate_proj.scores', 'model.layers.17.mlp.up_proj.scores', 'model.layers.17.self_attn.k_proj.scores', 'model.layers.17.self_attn.o_proj.scores', 'model.layers.17.self_attn.q_proj.scores', 'model.layers.17.self_attn.v_proj.scores', 'model.layers.18.mlp.down_proj.scores', 'model.layers.18.mlp.gate_proj.scores', 'model.layers.18.mlp.up_proj.scores', 'model.layers.18.self_attn.k_proj.scores', 'model.layers.18.self_attn.o_proj.scores', 'model.layers.18.self_attn.q_proj.scores', 'model.layers.18.self_attn.v_proj.scores', 'model.layers.19.mlp.down_proj.scores', 'model.layers.19.mlp.gate_proj.scores', 'model.layers.19.mlp.up_proj.scores', 'model.layers.19.self_attn.k_proj.scores', 'model.layers.19.self_attn.o_proj.scores', 'model.layers.19.self_attn.q_proj.scores', 'model.layers.19.self_attn.v_proj.scores', 'model.layers.2.mlp.down_proj.scores', 'model.layers.2.mlp.gate_proj.scores', 'model.layers.2.mlp.up_proj.scores', 'model.layers.2.self_attn.k_proj.scores', 'model.layers.2.self_attn.o_proj.scores', 'model.layers.2.self_attn.q_proj.scores', 'model.layers.2.self_attn.v_proj.scores', 'model.layers.20.mlp.down_proj.scores', 'model.layers.20.mlp.gate_proj.scores', 'model.layers.20.mlp.up_proj.scores', 'model.layers.20.self_attn.k_proj.scores', 'model.layers.20.self_attn.o_proj.scores', 'model.layers.20.self_attn.q_proj.scores', 'model.layers.20.self_attn.v_proj.scores', 'model.layers.21.mlp.down_proj.scores', 'model.layers.21.mlp.gate_proj.scores', 'model.layers.21.mlp.up_proj.scores', 'model.layers.21.self_attn.k_proj.scores', 'model.layers.21.self_attn.o_proj.scores', 'model.layers.21.self_attn.q_proj.scores', 'model.layers.21.self_attn.v_proj.scores', 'model.layers.22.mlp.down_proj.scores', 'model.layers.22.mlp.gate_proj.scores', 'model.layers.22.mlp.up_proj.scores', 'model.layers.22.self_attn.k_proj.scores', 'model.layers.22.self_attn.o_proj.scores', 'model.layers.22.self_attn.q_proj.scores', 'model.layers.22.self_attn.v_proj.scores', 'model.layers.23.mlp.down_proj.scores', 'model.layers.23.mlp.gate_proj.scores', 'model.layers.23.mlp.up_proj.scores', 'model.layers.23.self_attn.k_proj.scores', 'model.layers.23.self_attn.o_proj.scores', 'model.layers.23.self_attn.q_proj.scores', 'model.layers.23.self_attn.v_proj.scores', 'model.layers.3.mlp.down_proj.scores', 'model.layers.3.mlp.gate_proj.scores', 'model.layers.3.mlp.up_proj.scores', 'model.layers.3.self_attn.k_proj.scores', 'model.layers.3.self_attn.o_proj.scores', 'model.layers.3.self_attn.q_proj.scores', 'model.layers.3.self_attn.v_proj.scores', 'model.layers.4.mlp.down_proj.scores', 'model.layers.4.mlp.gate_proj.scores', 'model.layers.4.mlp.up_proj.scores', 'model.layers.4.self_attn.k_proj.scores', 'model.layers.4.self_attn.o_proj.scores', 'model.layers.4.self_attn.q_proj.scores', 'model.layers.4.self_attn.v_proj.scores', 'model.layers.5.mlp.down_proj.scores', 'model.layers.5.mlp.gate_proj.scores', 'model.layers.5.mlp.up_proj.scores', 'model.layers.5.self_attn.k_proj.scores', 'model.layers.5.self_attn.o_proj.scores', 'model.layers.5.self_attn.q_proj.scores', 'model.layers.5.self_attn.v_proj.scores', 'model.layers.6.mlp.down_proj.scores', 'model.layers.6.mlp.gate_proj.scores', 'model.layers.6.mlp.up_proj.scores', 'model.layers.6.self_attn.k_proj.scores', 'model.layers.6.self_attn.o_proj.scores', 'model.layers.6.self_attn.q_proj.scores', 'model.layers.6.self_attn.v_proj.scores', 'model.layers.7.mlp.down_proj.scores', 'model.layers.7.mlp.gate_proj.scores', 'model.layers.7.mlp.up_proj.scores', 'model.layers.7.self_attn.k_proj.scores', 'model.layers.7.self_attn.o_proj.scores', 'model.layers.7.self_attn.q_proj.scores', 'model.layers.7.self_attn.v_proj.scores', 'model.layers.8.mlp.down_proj.scores', 'model.layers.8.mlp.gate_proj.scores', 'model.layers.8.mlp.up_proj.scores', 'model.layers.8.self_attn.k_proj.scores', 'model.layers.8.self_attn.o_proj.scores', 'model.layers.8.self_attn.q_proj.scores', 'model.layers.8.self_attn.v_proj.scores', 'model.layers.9.mlp.down_proj.scores', 'model.layers.9.mlp.gate_proj.scores', 'model.layers.9.mlp.up_proj.scores', 'model.layers.9.self_attn.k_proj.scores', 'model.layers.9.self_attn.o_proj.scores', 'model.layers.9.self_attn.q_proj.scores', 'model.layers.9.self_attn.v_proj.scores'] +You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +loading language model from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/language_model +[2025-10-18 05:55:55,630] [INFO] [partition_parameters.py:343:__exit__] finished initializing model - num_params = 907, num_elems = 1.42B +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading vision tower from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/vision_tower +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +Loading connector from /nfs/ywang29/TinyLLaVA/checkpoints/tiny-llava-Qwen2.5-0.5B-siglip-so400m-patch14-384-pretrain/connector/pytorch_model.bin... +TinyLlavaForConditionalGeneration( + (language_model): Qwen2ForCausalLM( + (model): Qwen2Model( + (embed_tokens): Embedding(151936, 896) + (layers): ModuleList( + (0-23): 24 x Qwen2DecoderLayer( + (self_attn): Qwen2FlashAttention2( + (q_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + (k_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (v_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=128, bias=True) + (o_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=False) + (rotary_emb): Qwen2RotaryEmbedding() + ) + (mlp): Qwen2MLP( + (gate_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (up_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=4864, bias=False) + (down_proj): SupermaskLinearSparsity_SoftForward_Normal(in_features=4864, out_features=896, bias=False) + (act_fn): SiLU() + ) + (input_layernorm): Qwen2RMSNorm() + (post_attention_layernorm): Qwen2RMSNorm() + ) + ) + (norm): Qwen2RMSNorm() + ) + (lm_head): Linear(in_features=896, out_features=151936, bias=False) + ) + (vision_tower): SIGLIPVisionTower( + (_vision_tower): SiglipVisionModel( + (vision_model): SiglipVisionTransformer( + (embeddings): SiglipVisionEmbeddings( + (patch_embedding): Conv2d(3, 1152, kernel_size=(14, 14), stride=(14, 14), padding=valid) + (position_embedding): Embedding(729, 1152) + ) + (encoder): SiglipEncoder( + (layers): ModuleList( + (0-26): 27 x SiglipEncoderLayer( + (self_attn): SiglipAttention( + (k_proj): Linear(in_features=1152, out_features=1152, bias=True) + (v_proj): Linear(in_features=1152, out_features=1152, bias=True) + (q_proj): Linear(in_features=1152, out_features=1152, bias=True) + (out_proj): Linear(in_features=1152, out_features=1152, bias=True) + ) + (layer_norm1): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + (layer_norm2): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + ) + ) + ) + (post_layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (head): SiglipMultiheadAttentionPoolingHead( + (attention): MultiheadAttention( + (out_proj): NonDynamicallyQuantizableLinear(in_features=1152, out_features=1152, bias=True) + ) + (layernorm): LayerNorm((1152,), eps=1e-06, elementwise_affine=True) + (mlp): SiglipMLP( + (activation_fn): PytorchGELUTanh() + (fc1): Linear(in_features=1152, out_features=4304, bias=True) + (fc2): Linear(in_features=4304, out_features=1152, bias=True) + ) + ) + ) + ) + ) + (connector): MLPConnector( + (_connector): Sequential( + (0): SupermaskLinearSparsity_SoftForward_Normal(in_features=1152, out_features=896, bias=True) + (1): GELU(approximate='none') + (2): SupermaskLinearSparsity_SoftForward_Normal(in_features=896, out_features=896, bias=True) + ) + ) +) +Pre-training init language_model.model.layers.0.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.0.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.1.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.2.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.3.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.4.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.5.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.6.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.7.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.8.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.9.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.10.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.11.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.12.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.13.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.14.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.15.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.16.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.17.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.18.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.19.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.20.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.21.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.22.mlp.down_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.q_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.k_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.v_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.self_attn.o_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.gate_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.up_proj.scores: Mean=9.000000 +Pre-training init language_model.model.layers.23.mlp.down_proj.scores: Mean=9.000000 +Pre-training init connector._connector.0.scores: Mean=9.000005 +Pre-training init connector._connector.2.scores: Mean=8.999970 +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +Randomly sampled 66529 training samples (10.0% of 665298 total samples) +2025-10-18 05:56:13,787 | INFO: Total Parameters: 1283756736, Total Trainable Parameters: 359661568 +2025-10-18 05:56:13,792 | INFO: Trainable Parameters: +language_model.model.layers.0.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.0.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.0.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.0.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.0.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.1.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.1.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.1.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.1.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.1.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.2.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.2.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.2.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.2.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.2.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.3.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.3.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.3.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.3.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.3.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.4.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.4.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.4.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.4.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.4.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.5.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.5.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.5.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.5.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.5.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.6.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.6.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.6.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.6.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.6.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.7.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.7.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.7.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.7.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.7.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.8.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.8.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.8.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.8.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.8.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.9.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.9.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.9.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.9.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.9.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.10.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.10.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.10.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.10.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.10.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.11.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.11.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.11.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.11.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.11.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.12.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.12.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.12.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.12.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.12.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.13.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.13.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.13.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.13.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.13.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.14.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.14.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.14.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.14.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.14.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.15.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.15.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.15.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.15.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.15.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.16.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.16.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.16.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.16.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.16.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.17.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.17.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.17.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.17.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.17.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.18.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.18.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.18.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.18.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.18.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.19.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.19.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.19.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.19.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.19.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.20.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.20.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.20.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.20.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.20.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.21.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.21.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.21.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.21.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.21.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.22.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.22.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.22.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.22.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.22.mlp.down_proj.scores: 4358144 parameters +language_model.model.layers.23.self_attn.q_proj.scores: 802816 parameters +language_model.model.layers.23.self_attn.k_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.v_proj.scores: 114688 parameters +language_model.model.layers.23.self_attn.o_proj.scores: 802816 parameters +language_model.model.layers.23.mlp.gate_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.up_proj.scores: 4358144 parameters +language_model.model.layers.23.mlp.down_proj.scores: 4358144 parameters +connector._connector.0.scores: 1032192 parameters +connector._connector.2.scores: 802816 parameters +Parameter Offload: Total persistent parameters: 486464 in 403 params + 0%| | 0/520 [00:007->6 [1] -1/-1/-1->7->6 [2] -1/-1/-1->7->6 [3] -1/-1/-1->7->6 [4] -1/-1/-1->7->6 [5] -1/-1/-1->7->6 [6] -1/-1/-1->7->6 [7] -1/-1/-1->7->6 [8] -1/-1/-1->7->6 [9] -1/-1/-1->7->6 [10] -1/-1/-1->7->6 [11] -1/-1/-1->7->6 [12] -1/-1/-1->7->6 [13] -1/-1/-1->7->6 [14] -1/-1/-1->7->6 [15] -1/-1/-1->7->6 [16] -1/-1/-1->7->6 [17] -1/-1/-1->7->6 [18] -1/-1/-1->7->6 [19] -1/-1/-1->7->6 [20] -1/-1/-1->7->6 [21] -1/-1/-1->7->6 [22] -1/-1/-1->7->6 [23] -1/-1/-1->7->6 +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Trees [0] 6/-1/-1->5->4 [1] 6/-1/-1->5->4 [2] 6/-1/-1->5->4 [3] 6/-1/-1->5->4 [4] 6/-1/-1->5->4 [5] 6/-1/-1->5->4 [6] 6/-1/-1->5->4 [7] 6/-1/-1->5->4 [8] 6/-1/-1->5->4 [9] 6/-1/-1->5->4 [10] 6/-1/-1->5->4 [11] 6/-1/-1->5->4 [12] 6/-1/-1->5->4 [13] 6/-1/-1->5->4 [14] 6/-1/-1->5->4 [15] 6/-1/-1->5->4 [16] 6/-1/-1->5->4 [17] 6/-1/-1->5->4 [18] 6/-1/-1->5->4 [19] 6/-1/-1->5->4 [20] 6/-1/-1->5->4 [21] 6/-1/-1->5->4 [22] 6/-1/-1->5->4 [23] 6/-1/-1->5->4 +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Trees [0] 7/-1/-1->6->5 [1] 7/-1/-1->6->5 [2] 7/-1/-1->6->5 [3] 7/-1/-1->6->5 [4] 7/-1/-1->6->5 [5] 7/-1/-1->6->5 [6] 7/-1/-1->6->5 [7] 7/-1/-1->6->5 [8] 7/-1/-1->6->5 [9] 7/-1/-1->6->5 [10] 7/-1/-1->6->5 [11] 7/-1/-1->6->5 [12] 7/-1/-1->6->5 [13] 7/-1/-1->6->5 [14] 7/-1/-1->6->5 [15] 7/-1/-1->6->5 [16] 7/-1/-1->6->5 [17] 7/-1/-1->6->5 [18] 7/-1/-1->6->5 [19] 7/-1/-1->6->5 [20] 7/-1/-1->6->5 [21] 7/-1/-1->6->5 [22] 7/-1/-1->6->5 [23] 7/-1/-1->6->5 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 01/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1 [2] 3/-1/-1->2->1 [3] 3/-1/-1->2->1 [4] 3/-1/-1->2->1 [5] 3/-1/-1->2->1 [6] 3/-1/-1->2->1 [7] 3/-1/-1->2->1 [8] 3/-1/-1->2->1 [9] 3/-1/-1->2->1 [10] 3/-1/-1->2->1 [11] 3/-1/-1->2->1 [12] 3/-1/-1->2->1 [13] 3/-1/-1->2->1 [14] 3/-1/-1->2->1 [15] 3/-1/-1->2->1 [16] 3/-1/-1->2->1 [17] 3/-1/-1->2->1 [18] 3/-1/-1->2->1 [19] 3/-1/-1->2->1 [20] 3/-1/-1->2->1 [21] 3/-1/-1->2->1 [22] 3/-1/-1->2->1 [23] 3/-1/-1->2->1 +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0 [2] 2/-1/-1->1->0 [3] 2/-1/-1->1->0 [4] 2/-1/-1->1->0 [5] 2/-1/-1->1->0 [6] 2/-1/-1->1->0 [7] 2/-1/-1->1->0 [8] 2/-1/-1->1->0 [9] 2/-1/-1->1->0 [10] 2/-1/-1->1->0 [11] 2/-1/-1->1->0 [12] 2/-1/-1->1->0 [13] 2/-1/-1->1->0 [14] 2/-1/-1->1->0 [15] 2/-1/-1->1->0 [16] 2/-1/-1->1->0 [17] 2/-1/-1->1->0 [18] 2/-1/-1->1->0 [19] 2/-1/-1->1->0 [20] 2/-1/-1->1->0 [21] 2/-1/-1->1->0 [22] 2/-1/-1->1->0 [23] 2/-1/-1->1->0 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 02/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Trees [0] 5/-1/-1->4->3 [1] 5/-1/-1->4->3 [2] 5/-1/-1->4->3 [3] 5/-1/-1->4->3 [4] 5/-1/-1->4->3 [5] 5/-1/-1->4->3 [6] 5/-1/-1->4->3 [7] 5/-1/-1->4->3 [8] 5/-1/-1->4->3 [9] 5/-1/-1->4->3 [10] 5/-1/-1->4->3 [11] 5/-1/-1->4->3 [12] 5/-1/-1->4->3 [13] 5/-1/-1->4->3 [14] 5/-1/-1->4->3 [15] 5/-1/-1->4->3 [16] 5/-1/-1->4->3 [17] 5/-1/-1->4->3 [18] 5/-1/-1->4->3 [19] 5/-1/-1->4->3 [20] 5/-1/-1->4->3 [21] 5/-1/-1->4->3 [22] 5/-1/-1->4->3 [23] 5/-1/-1->4->3 +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 03/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 04/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 05/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 06/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 07/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Trees [0] 4/-1/-1->3->2 [1] 4/-1/-1->3->2 [2] 4/-1/-1->3->2 [3] 4/-1/-1->3->2 [4] 4/-1/-1->3->2 [5] 4/-1/-1->3->2 [6] 4/-1/-1->3->2 [7] 4/-1/-1->3->2 [8] 4/-1/-1->3->2 [9] 4/-1/-1->3->2 [10] 4/-1/-1->3->2 [11] 4/-1/-1->3->2 [12] 4/-1/-1->3->2 [13] 4/-1/-1->3->2 [14] 4/-1/-1->3->2 [15] 4/-1/-1->3->2 [16] 4/-1/-1->3->2 [17] 4/-1/-1->3->2 [18] 4/-1/-1->3->2 [19] 4/-1/-1->3->2 [20] 4/-1/-1->3->2 [21] 4/-1/-1->3->2 [22] 4/-1/-1->3->2 [23] 4/-1/-1->3->2 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 08/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 09/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 10/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 11/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 12/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 13/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 14/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 15/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 16/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 17/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 18/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 19/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 20/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 21/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 22/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 23/24 : 0 1 2 3 4 5 6 7 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] 1/-1/-1->0->-1 [2] 1/-1/-1->0->-1 [3] 1/-1/-1->0->-1 [4] 1/-1/-1->0->-1 [5] 1/-1/-1->0->-1 [6] 1/-1/-1->0->-1 [7] 1/-1/-1->0->-1 [8] 1/-1/-1->0->-1 [9] 1/-1/-1->0->-1 [10] 1/-1/-1->0->-1 [11] 1/-1/-1->0->-1 [12] 1/-1/-1->0->-1 [13] 1/-1/-1->0->-1 [14] 1/-1/-1->0->-1 [15] 1/-1/-1->0->-1 [16] 1/-1/-1->0->-1 [17] 1/-1/-1->0->-1 [18] 1/-1/-1->0->-1 [19] 1/-1/-1->0->-1 [20] 1/-1/-1->0->-1 [21] 1/-1/-1->0->-1 [22] 1/-1/-1->0->-1 [23] 1/-1/-1->0->-1 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO P2P Chunksize set to 524288 +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 00/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 00/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 16/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 00/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 00/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 17/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 01/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 18/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 01/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 19/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 20/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 16/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 21/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 17/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 22/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 18/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 23/0 : 4[4] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 19/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 20/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 21/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 22/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Channel 23/0 : 0[0] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 16/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 00/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 17/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 16/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 16/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 16/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 01/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 18/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 17/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 17/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 16/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 17/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 19/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 18/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 18/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 17/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 18/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 20/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 19/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 19/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 18/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 19/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 20/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 21/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 20/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 19/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 20/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 22/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 21/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 21/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 20/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 21/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 23/0 : 5[5] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 22/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 22/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 21/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 22/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 23/0 : 6[6] -> 7[7] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 23/0 : 1[1] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 22/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 23/0 : 3[3] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 23/0 : 2[2] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 16/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 17/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 18/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 19/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 20/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 21/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 22/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 23/0 : 7[7] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Connected all rings +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 16/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 17/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 18/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 19/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 06/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 15/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 20/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 07/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 16/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 21/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 08/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 17/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 22/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 09/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 18/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Channel 23/0 : 7[7] -> 6[6] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 10/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 19/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 11/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 20/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 12/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 21/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 13/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 22/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 14/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Channel 23/0 : 2[2] -> 1[1] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 15/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 06/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 16/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 07/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 17/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 08/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 18/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 09/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 19/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 10/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 20/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 11/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 21/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 12/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 22/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 13/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 14/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Channel 23/0 : 4[4] -> 3[3] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 15/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 16/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 17/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 18/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 08/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 19/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 20/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 09/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 21/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 10/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 22/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 11/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Channel 23/0 : 6[6] -> 5[5] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 12/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 13/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 14/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 15/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 16/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 17/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 06/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 18/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 07/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 10/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 08/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 19/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 11/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 09/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 20/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 12/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 10/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 13/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 21/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 11/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 14/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 22/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 12/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 13/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Channel 23/0 : 3[3] -> 2[2] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 15/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 14/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 16/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 15/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 17/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 16/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 18/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 17/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 19/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 18/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 20/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 19/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 21/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 20/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 22/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 21/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Channel 23/0 : 1[1] -> 0[0] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 22/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Channel 23/0 : 5[5] -> 4[4] via P2P/CUMEM/read +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008836:1015452 [0] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008837:1015457 [1] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008838:1015453 [2] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008839:1015458 [3] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008840:1015455 [4] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008841:1015454 [5] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 +ywang29-vrdb-test1-worker-0:1008842:1015456 [6] NCCL INFO 24 coll channels, 24 collnet channels, 0 nvls channels, 32 p2p channels, 32 p2p channels per peer +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO Connected all trees +ywang29-vrdb-test1-worker-0:1008843:1015459 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512 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[00:21<52:20, 6.07s/it] {'loss': 2.2139, 'grad_norm': 0.010369866848340013, 'learning_rate': 0.037500000000000006, 'epoch': 0.01} + 1%| | 3/520 [00:21<52:20, 6.07s/it] 1%| | 4/520 [00:25<44:02, 5.12s/it] {'loss': 2.0892, 'grad_norm': 0.008668953406891331, 'learning_rate': 0.05, 'epoch': 0.01} + 1%| | 4/520 [00:25<44:02, 5.12s/it] 1%| | 5/520 [00:28<39:20, 4.58s/it] {'loss': 2.2467, 'grad_norm': 0.009585058982248255, 'learning_rate': 0.0625, 'epoch': 0.01} + 1%| | 5/520 [00:28<39:20, 4.58s/it] 1%| | 6/520 [00:32<36:32, 4.27s/it] {'loss': 1.5773, 'grad_norm': 0.0032132295250073325, 'learning_rate': 0.07500000000000001, 'epoch': 0.01} + 1%| | 6/520 [00:32<36:32, 4.27s/it] 1%|▏ | 7/520 [00:36<34:42, 4.06s/it] {'loss': 1.6824, 'grad_norm': 0.0042536272699128, 'learning_rate': 0.08750000000000001, 'epoch': 0.01} + 1%|▏ | 7/520 [00:36<34:42, 4.06s/it] 2%|▏ | 8/520 [00:40<35:07, 4.12s/it] {'loss': 1.5959, 'grad_norm': 0.0019565253579695375, 'learning_rate': 0.1, 'epoch': 0.02} + 2%|▏ | 8/520 [00:40<35:07, 4.12s/it] 2%|▏ | 9/520 [00:44<35:08, 4.13s/it] {'loss': 1.6365, 'grad_norm': 0.001628243642926101, 'learning_rate': 0.1125, 'epoch': 0.02} + 2%|▏ | 9/520 [00:44<35:08, 4.13s/it] 2%|▏ | 10/520 [00:48<33:44, 3.97s/it] {'loss': 1.4431, 'grad_norm': 0.001575793746658227, 'learning_rate': 0.125, 'epoch': 0.02} + 2%|▏ | 10/520 [00:48<33:44, 3.97s/it] 2%|▏ | 11/520 [00:52<33:15, 3.92s/it] {'loss': 1.4823, 'grad_norm': 0.0011690529594321182, 'learning_rate': 0.1375, 'epoch': 0.02} + 2%|▏ | 11/520 [00:52<33:15, 3.92s/it] 2%|▏ | 12/520 [00:55<32:32, 3.84s/it] {'loss': 1.3735, 'grad_norm': 0.0009365364682881286, 'learning_rate': 0.15000000000000002, 'epoch': 0.02} + 2%|▏ | 12/520 [00:55<32:32, 3.84s/it][2025-10-18 05:57:18,129] [WARNING] [stage3.py:2069:step] 1 pytorch allocator cache flushes since last step. this happens when there is high memory pressure and is detrimental to performance. if this is happening frequently consider adjusting settings to reduce memory consumption. 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23/520 [01:37<31:20, 3.78s/it] {'loss': 1.409, 'grad_norm': 0.0008482202125373304, 'learning_rate': 0.1999048221581858, 'epoch': 0.04} + 4%|▍ | 23/520 [01:37<31:20, 3.78s/it] 5%|▍ | 24/520 [01:40<31:22, 3.80s/it] {'loss': 1.3285, 'grad_norm': 0.0007155732290016687, 'learning_rate': 0.19987569212189224, 'epoch': 0.05} + 5%|▍ | 24/520 [01:40<31:22, 3.80s/it] 5%|▍ | 25/520 [01:44<31:03, 3.77s/it] {'loss': 1.4023, 'grad_norm': 0.0008061197578090407, 'learning_rate': 0.19984268150178167, 'epoch': 0.05} + 5%|▍ | 25/520 [01:44<31:03, 3.77s/it] 5%|▌ | 26/520 [01:48<30:40, 3.73s/it] {'loss': 1.3435, 'grad_norm': 0.0006207429604029264, 'learning_rate': 0.1998057915804532, 'epoch': 0.05} + 5%|▌ | 26/520 [01:48<30:40, 3.73s/it] 5%|▌ | 27/520 [01:51<30:25, 3.70s/it] {'loss': 1.2709, 'grad_norm': 0.0006232300264940507, 'learning_rate': 0.1997650237912329, 'epoch': 0.05} + 5%|▌ | 27/520 [01:51<30:25, 3.70s/it] 5%|▌ | 28/520 [01:55<30:18, 3.70s/it] {'loss': 1.297, 'grad_norm': 0.0006683332169943914, 'learning_rate': 0.199720379718118, 'epoch': 0.05} + 5%|▌ | 28/520 [01:55<30:18, 3.70s/it] 6%|▌ | 29/520 [01:59<30:12, 3.69s/it] {'loss': 1.3147, 'grad_norm': 0.0006531349069302493, 'learning_rate': 0.19967186109571552, 'epoch': 0.06} + 6%|▌ | 29/520 [01:59<30:12, 3.69s/it] 6%|▌ | 30/520 [02:02<30:03, 3.68s/it] {'loss': 1.3932, 'grad_norm': 0.0005783126100084805, 'learning_rate': 0.19961946980917455, 'epoch': 0.06} + 6%|▌ | 30/520 [02:02<30:03, 3.68s/it] 6%|▌ | 31/520 [02:06<30:06, 3.69s/it] {'loss': 1.2784, 'grad_norm': 0.0005608869044675318, 'learning_rate': 0.1995632078941134, 'epoch': 0.06} + 6%|▌ | 31/520 [02:06<30:06, 3.69s/it] 6%|▌ | 32/520 [02:10<30:08, 3.71s/it] {'loss': 1.2371, 'grad_norm': 0.0006789392826068554, 'learning_rate': 0.19950307753654017, 'epoch': 0.06} + 6%|▌ | 32/520 [02:10<30:08, 3.71s/it] 6%|▋ | 33/520 [02:14<29:58, 3.69s/it] {'loss': 1.2805, 'grad_norm': 0.0006823715367634065, 'learning_rate': 0.19943908107276798, 'epoch': 0.06} + 6%|▋ | 33/520 [02:14<29:58, 3.69s/it] 7%|▋ | 34/520 [02:17<29:51, 3.69s/it] {'loss': 1.2702, 'grad_norm': 0.0006971998604592731, 'learning_rate': 0.19937122098932428, 'epoch': 0.07} + 7%|▋ | 34/520 [02:17<29:51, 3.69s/it] 7%|▋ | 35/520 [02:21<29:46, 3.68s/it] {'loss': 1.2804, 'grad_norm': 0.0006878968693306654, 'learning_rate': 0.19929949992285395, 'epoch': 0.07} + 7%|▋ | 35/520 [02:21<29:46, 3.68s/it] 7%|▋ | 36/520 [02:25<29:44, 3.69s/it] {'loss': 1.3687, 'grad_norm': 0.0006298067965892617, 'learning_rate': 0.19922392066001723, 'epoch': 0.07} + 7%|▋ | 36/520 [02:25<29:44, 3.69s/it] 7%|▋ | 37/520 [02:28<29:37, 3.68s/it] {'loss': 1.3618, 'grad_norm': 0.000616459380768698, 'learning_rate': 0.19914448613738106, 'epoch': 0.07} + 7%|▋ | 37/520 [02:28<29:37, 3.68s/it] 7%|▋ | 38/520 [02:32<29:28, 3.67s/it] {'loss': 1.4425, 'grad_norm': 0.0006201955032152143, 'learning_rate': 0.1990611994413053, 'epoch': 0.07} + 7%|▋ | 38/520 [02:32<29:28, 3.67s/it] 8%|▊ | 39/520 [02:36<29:23, 3.67s/it] {'loss': 1.3045, 'grad_norm': 0.0007092976298647951, 'learning_rate': 0.19897406380782262, 'epoch': 0.07} + 8%|▊ | 39/520 [02:36<29:23, 3.67s/it] 8%|▊ | 40/520 [02:39<29:17, 3.66s/it] {'loss': 1.3362, 'grad_norm': 0.0005727338467009415, 'learning_rate': 0.19888308262251286, 'epoch': 0.08} + 8%|▊ | 40/520 [02:39<29:17, 3.66s/it] 8%|▊ | 41/520 [02:43<29:13, 3.66s/it] {'loss': 1.3163, 'grad_norm': 0.0006464291803792414, 'learning_rate': 0.19878825942037148, 'epoch': 0.08} + 8%|▊ | 41/520 [02:43<29:13, 3.66s/it] 8%|▊ | 42/520 [02:47<29:07, 3.66s/it] {'loss': 1.3106, 'grad_norm': 0.0007848468283634711, 'learning_rate': 0.19868959788567211, 'epoch': 0.08} + 8%|▊ | 42/520 [02:47<29:07, 3.66s/it] 8%|▊ | 43/520 [02:50<29:09, 3.67s/it] {'loss': 1.2497, 'grad_norm': 0.0005705474946914178, 'learning_rate': 0.1985871018518236, 'epoch': 0.08} + 8%|▊ | 43/520 [02:50<29:09, 3.67s/it] 8%|▊ | 44/520 [02:54<29:00, 3.66s/it] {'loss': 1.3526, 'grad_norm': 0.0005834300419208111, 'learning_rate': 0.19848077530122082, 'epoch': 0.08} + 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{'loss': 1.3253, 'grad_norm': 0.0006036071319570443, 'learning_rate': 0.19776261689193048, 'epoch': 0.1} + 10%|▉ | 50/520 [03:16<28:44, 3.67s/it] 10%|▉ | 51/520 [03:20<29:03, 3.72s/it] {'loss': 1.2646, 'grad_norm': 0.000678314471024274, 'learning_rate': 0.19762960071199334, 'epoch': 0.1} + 10%|▉ | 51/520 [03:20<29:03, 3.72s/it] 10%|█ | 52/520 [03:23<29:13, 3.75s/it] {'loss': 1.3875, 'grad_norm': 0.0007726639990008913, 'learning_rate': 0.19749279121818236, 'epoch': 0.1} + 10%|█ | 52/520 [03:23<29:13, 3.75s/it] 10%|█ | 53/520 [03:27<29:08, 3.74s/it] {'loss': 1.3671, 'grad_norm': 0.000659369966659598, 'learning_rate': 0.19735219372611235, 'epoch': 0.1} + 10%|█ | 53/520 [03:27<29:08, 3.74s/it] 10%|█ | 54/520 [03:31<28:51, 3.72s/it] {'loss': 1.3037, 'grad_norm': 0.0006126610611182303, 'learning_rate': 0.19720781369857746, 'epoch': 0.1} + 10%|█ | 54/520 [03:31<28:51, 3.72s/it] 11%|█ | 55/520 [03:34<28:34, 3.69s/it] {'loss': 1.2621, 'grad_norm': 0.0007195925044656497, 'learning_rate': 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