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nohup: ignoring input
+ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+ DATA_ROOT=/jizhicfs/bojoli
+ VISION_TOWER_NAME=clip-vit-large-patch14-336
+ MODEL_NAME=vicuna-7b-1.5
+ VISION_TOWER=/jizhicfs/bojoli/clip-vit-large-patch14-336
+ MODEL_PATH=/jizhicfs/bojoli/vicuna-7b-1.5
+ BASE_RUN_NAME=mmpe_vicuna-7b-1.5_clip-vit-large-patch14-336
+ RUN_NAME=final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336
+ PROMPT_VERSION=v1
+ EVAL_MODEL=../checkpoints/final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336
+ BENCHMARKS=mmbench_en
+ python3 -m accelerate.commands.launch --num_processes=8 --main_process_port 12899 -m lmms_eval --model llava --model_args pretrained=../checkpoints/final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336,conv_template=v1 --tasks mmbench_en --batch_size 1 --log_samples --log_samples_suffix final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336 --output_path ./logs/
The following values were not passed to `accelerate launch` and had defaults used instead:
More than one GPU was found, enabling multi-GPU training.
If this was unintended please pass in `--num_processes=1`.
`--num_machines` was set to a value of `1`
`--mixed_precision` was set to a value of `'no'`
`--dynamo_backend` was set to a value of `'no'`
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
[32m2025-05-19 01:44:28.140[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.206[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.914[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.916[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.922[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.923[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.931[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
[32m2025-05-19 01:44:28.941[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate[0m:[36m294[0m - [1mVerbosity set to INFO[0m
Detected kernel version 5.4.119, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
[32m2025-05-19 01:44:31.949[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:31.950[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:31.955[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.253[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.253[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.260[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.902[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.902[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.907[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.960[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.960[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.964[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.965[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.966[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.969[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.970[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.971[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.972[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.972[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m377[0m - [1mEvaluation tracker args: {'output_path': './logs/'}[0m
[32m2025-05-19 01:44:32.973[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.973[0m | [1mINFO [0m | [36m__main__[0m:[36mcli_evaluate_single[0m:[36m466[0m - [1mSelected Tasks: ['mmbench_en'][0m
[32m2025-05-19 01:44:32.977[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.982[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
[32m2025-05-19 01:44:32.982[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36msimple_evaluate[0m:[36m155[0m - [1mSetting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234[0m
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]Detected kernel version 5.4.119, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
Rank 0: Loaded LLaVA model: ../checkpoints/final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
Rank 0: Loading vision tower: /jizhicfs/bojoli/clip-vit-large-patch14-336
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Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]
Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]You are using a model of type llama to instantiate a model of type llava_llama. This is not supported for all configurations of models and can yield errors.
Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]
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[2025-05-19 01:45:00,372] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
Loading checkpoint shards: 100%|██████████| 3/3 [00:14<00:00, 4.62s/it]
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[2025-05-19 01:45:01,621] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[32m2025-05-19 01:45:02.002[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 2...[0m
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[32m2025-05-19 01:45:03.150[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 6...[0m
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[2025-05-19 01:45:03,657] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-19 01:45:03,662] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-19 01:45:03,670] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Rank 0: Model Class: LlavaLlamaForCausalLM
[2025-05-19 01:45:03,857] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-05-19 01:45:03,970] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
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[2025-05-19 01:45:05,606] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[32m2025-05-19 01:45:06.189[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 4...[0m
[32m2025-05-19 01:45:06.198[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 3...[0m
[32m2025-05-19 01:45:06.199[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 5...[0m
[32m2025-05-19 01:45:06.286[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_dev on rank 1...[0m
[32m2025-05-19 01:45:06.341[0m | [1mINFO [0m | [36mlmms_eval.models.llava[0m:[36m__init__[0m:[36m134[0m - [1mUsing 8 devices with data parallelism[0m
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[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
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TENCENT64:79957:80271 [4] NCCL INFO Channel 01/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 01/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 02/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 02/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 02/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 02/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 02/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 02/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 02/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 02/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 03/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 03/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 03/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 03/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 03/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 03/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 03/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 03/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 04/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 04/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 04/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 04/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 04/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 04/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 04/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 04/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 05/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 05/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 05/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 05/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 05/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 05/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 05/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 05/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 06/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 06/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 06/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 06/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 06/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 06/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 06/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 06/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 07/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 07/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 07/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 07/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 07/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 07/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 07/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 07/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 08/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 08/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 08/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 08/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 08/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 08/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 08/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 08/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 09/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 09/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 09/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 09/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 09/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 09/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 09/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 09/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 10/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 10/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 10/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 10/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 10/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 10/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 10/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 10/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 11/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 11/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 11/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 11/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 11/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 11/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 11/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 11/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 12/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 12/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 12/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 12/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 12/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 12/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 12/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 12/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 13/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 13/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 13/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 13/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 13/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 13/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 13/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 13/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 14/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 14/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 14/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 14/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 14/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 14/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 14/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 14/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 15/0 : 3[3] -> 4[4] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 15/0 : 5[5] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 15/0 : 2[2] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 15/0 : 7[7] -> 0[0] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 15/0 : 4[4] -> 5[5] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 15/0 : 6[6] -> 7[7] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 15/0 : 1[1] -> 2[2] via P2P/CUMEM/read
TENCENT64:79953:80265 [0] NCCL INFO Channel 15/0 : 0[0] -> 1[1] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Connected all rings
TENCENT64:79954:80270 [1] NCCL INFO Connected all rings
TENCENT64:79953:80265 [0] NCCL INFO Connected all rings
TENCENT64:79956:80269 [3] NCCL INFO Connected all rings
TENCENT64:79957:80271 [4] NCCL INFO Connected all rings
TENCENT64:79960:80336 [7] NCCL INFO Connected all rings
TENCENT64:79958:80267 [5] NCCL INFO Connected all rings
TENCENT64:79959:80266 [6] NCCL INFO Connected all rings
TENCENT64:79960:80336 [7] NCCL INFO Channel 00/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 01/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 02/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 03/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 04/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 05/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 06/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 07/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 08/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 09/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 02/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 10/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 03/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 11/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 04/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 12/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 05/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 13/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 06/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 14/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 07/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 02/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 00/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79960:80336 [7] NCCL INFO Channel 15/0 : 7[7] -> 6[6] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 08/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 00/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 00/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 03/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 02/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 01/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 09/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 01/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 01/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 04/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 03/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 02/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 10/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 02/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 02/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 05/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 04/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 03/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 11/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 03/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 03/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 06/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 05/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 04/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 12/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 04/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 04/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 07/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 06/0 : 3[3] -> 2[2] via P2P/CUMEM/read
TENCENT64:79957:80271 [4] NCCL INFO Channel 05/0 : 4[4] -> 3[3] via P2P/CUMEM/read
TENCENT64:79955:80268 [2] NCCL INFO Channel 13/0 : 2[2] -> 1[1] via P2P/CUMEM/read
TENCENT64:79958:80267 [5] NCCL INFO Channel 05/0 : 5[5] -> 4[4] via P2P/CUMEM/read
TENCENT64:79959:80266 [6] NCCL INFO Channel 05/0 : 6[6] -> 5[5] via P2P/CUMEM/read
TENCENT64:79954:80270 [1] NCCL INFO Channel 08/0 : 1[1] -> 0[0] via P2P/CUMEM/read
TENCENT64:79956:80269 [3] NCCL INFO Channel 07/0 : 3[3] -> 2[2] via P2P/CUMEM/read
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TENCENT64:79955:80268 [2] NCCL INFO Channel 14/0 : 2[2] -> 1[1] via P2P/CUMEM/read
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TENCENT64:79954:80270 [1] NCCL INFO Channel 09/0 : 1[1] -> 0[0] via P2P/CUMEM/read
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TENCENT64:79953:80265 [0] NCCL INFO Connected all trees
TENCENT64:79953:80265 [0] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79953:80265 [0] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79954:80270 [1] NCCL INFO Connected all trees
TENCENT64:79954:80270 [1] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79954:80270 [1] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79955:80268 [2] NCCL INFO Connected all trees
TENCENT64:79955:80268 [2] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79955:80268 [2] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79956:80269 [3] NCCL INFO Connected all trees
TENCENT64:79956:80269 [3] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79956:80269 [3] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79957:80271 [4] NCCL INFO Connected all trees
TENCENT64:79957:80271 [4] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79957:80271 [4] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79958:80267 [5] NCCL INFO Connected all trees
TENCENT64:79959:80266 [6] NCCL INFO Connected all trees
TENCENT64:79960:80336 [7] NCCL INFO Connected all trees
TENCENT64:79958:80267 [5] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79958:80267 [5] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79959:80266 [6] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79960:80336 [7] NCCL INFO threadThresholds 8/8/64 | 64/8/64 | 512 | 512
TENCENT64:79959:80266 [6] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79960:80336 [7] NCCL INFO 16 coll channels, 0 collnet channels, 0 nvls channels, 16 p2p channels, 16 p2p channels per peer
TENCENT64:79957:80271 [4] NCCL INFO comm 0xf018850 rank 4 nranks 8 cudaDev 4 nvmlDev 4 busId 89000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79960:80336 [7] NCCL INFO comm 0xe630280 rank 7 nranks 8 cudaDev 7 nvmlDev 7 busId c9000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79958:80267 [5] NCCL INFO comm 0x11e9ea30 rank 5 nranks 8 cudaDev 5 nvmlDev 5 busId 8e000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79956:80269 [3] NCCL INFO comm 0xcd6dc30 rank 3 nranks 8 cudaDev 3 nvmlDev 3 busId 4d000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79959:80266 [6] NCCL INFO comm 0xf3037b0 rank 6 nranks 8 cudaDev 6 nvmlDev 6 busId c5000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79955:80268 [2] NCCL INFO comm 0xb2ff1f0 rank 2 nranks 8 cudaDev 2 nvmlDev 2 busId 49000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79953:80265 [0] NCCL INFO comm 0xd3ff140 rank 0 nranks 8 cudaDev 0 nvmlDev 0 busId 10000 commId 0x402b4d0350a71a1b - Init COMPLETE
TENCENT64:79954:80270 [1] NCCL INFO comm 0x1d2d43f0 rank 1 nranks 8 cudaDev 1 nvmlDev 1 busId 16000 commId 0x402b4d0350a71a1b - Init COMPLETE
[32m2025-05-19 01:45:13.639[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 0...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 7...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 2...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 6...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 3...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 4...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 1...[0m
[32m2025-05-19 01:45:13.640[0m | [1mINFO [0m | [36mlmms_eval.api.task[0m:[36mbuild_all_requests[0m:[36m425[0m - [1mBuilding contexts for mmbench_en_test on rank 5...[0m
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[32m2025-05-19 01:45:19.562[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36mevaluate[0m:[36m446[0m - [1mRunning generate_until requests[0m
[32m2025-05-19 01:45:19.562[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36mevaluate[0m:[36m446[0m - [1mRunning generate_until requests[0m
[32m2025-05-19 01:45:19.562[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36mevaluate[0m:[36m446[0m - [1mRunning generate_until requests[0m
[32m2025-05-19 01:45:19.562[0m | [1mINFO [0m | [36mlmms_eval.evaluator[0m:[36mevaluate[0m:[36m446[0m - [1mRunning generate_until requests[0m
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Model Responding: 0%| | 0/1376 [00:00<?, ?it/s]
Model Responding: 0%| | 1/1376 [00:01<35:46, 1.56s/it]
Model Responding: 0%| | 2/1376 [00:01<17:17, 1.32it/s]
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Model Responding: 0%| | 4/1376 [00:02<08:39, 2.64it/s]
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Model Responding: 0%| | 6/1376 [00:02<07:35, 3.01it/s]
Model Responding: 1%| | 7/1376 [00:03<07:19, 3.12it/s]
Model Responding: 1%| | 8/1376 [00:03<06:30, 3.50it/s]
Model Responding: 1%| | 9/1376 [00:03<05:47, 3.93it/s]
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Model Responding: 1%| | 13/1376 [00:04<04:23, 5.18it/s]
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Model Responding: 2%|▏ | 21/1376 [00:05<04:22, 5.16it/s]
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Model Responding: 2%|▏ | 23/1376 [00:06<03:52, 5.83it/s]
Model Responding: 2%|▏ | 24/1376 [00:06<03:43, 6.04it/s]
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Model Responding: 3%|▎ | 35/1376 [00:08<03:47, 5.89it/s]
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Model Responding: 3%|▎ | 37/1376 [00:08<04:21, 5.12it/s]
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Model Responding: 3%|▎ | 48/1376 [00:10<04:16, 5.19it/s]
Model Responding: 4%|▎ | 49/1376 [00:10<04:23, 5.03it/s]
Model Responding: 4%|▎ | 50/1376 [00:11<04:29, 4.93it/s]
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Model Responding: 4%|▍ | 54/1376 [00:11<04:07, 5.34it/s]
Model Responding: 4%|▍ | 55/1376 [00:11<04:02, 5.44it/s]
Model Responding: 4%|▍ | 56/1376 [00:12<03:59, 5.51it/s]
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Model Responding: 5%|▍ | 62/1376 [00:13<04:09, 5.27it/s]
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Model Responding: 5%|▍ | 66/1376 [00:14<05:15, 4.15it/s]
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Model Responding: 6%|▌ | 80/1376 [00:16<03:32, 6.09it/s]
Model Responding: 6%|▌ | 81/1376 [00:16<03:28, 6.22it/s]
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Model Responding: 6%|▌ | 84/1376 [00:17<03:51, 5.59it/s]
Model Responding: 6%|▌ | 85/1376 [00:17<04:03, 5.29it/s]
Model Responding: 6%|▋ | 86/1376 [00:17<04:04, 5.29it/s]
Model Responding: 6%|▋ | 87/1376 [00:17<03:50, 5.59it/s]
Model Responding: 6%|▋ | 88/1376 [00:18<03:46, 5.70it/s]
Model Responding: 6%|▋ | 89/1376 [00:18<03:42, 5.78it/s]
Model Responding: 7%|▋ | 90/1376 [00:18<03:29, 6.14it/s]
Model Responding: 7%|▋ | 91/1376 [00:18<03:45, 5.69it/s]
Model Responding: 7%|▋ | 92/1376 [00:18<04:43, 4.53it/s]
Model Responding: 7%|▋ | 93/1376 [00:19<04:23, 4.87it/s]
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Model Responding: 7%|▋ | 95/1376 [00:19<04:01, 5.31it/s]
Model Responding: 7%|▋ | 96/1376 [00:19<03:53, 5.49it/s]
Model Responding: 7%|▋ | 97/1376 [00:19<04:33, 4.68it/s]
Model Responding: 7%|▋ | 98/1376 [00:20<05:18, 4.02it/s]
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Model Responding: 8%|▊ | 104/1376 [00:21<05:13, 4.05it/s]
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Model Responding: 9%|▉ | 127/1376 [00:27<05:02, 4.13it/s]
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Model Responding: 10%|█ | 138/1376 [00:29<03:35, 5.75it/s]
Model Responding: 10%|█ | 139/1376 [00:29<04:08, 4.98it/s]
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Model Responding: 10%|█ | 141/1376 [00:29<03:52, 5.31it/s]
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Model Responding: 10%|█ | 144/1376 [00:30<04:38, 4.42it/s]
Model Responding: 11%|█ | 145/1376 [00:30<05:05, 4.03it/s]
Model Responding: 11%|█ | 146/1376 [00:31<05:16, 3.89it/s]
Model Responding: 11%|█ | 147/1376 [00:31<05:22, 3.82it/s]
Model Responding: 11%|█ | 148/1376 [00:31<05:28, 3.74it/s]
Model Responding: 11%|█ | 149/1376 [00:31<05:06, 4.00it/s]
Model Responding: 11%|█ | 150/1376 [00:32<04:42, 4.34it/s]
Model Responding: 11%|█ | 151/1376 [00:32<04:10, 4.88it/s]
Model Responding: 11%|█ | 152/1376 [00:32<04:32, 4.50it/s]
Model Responding: 11%|█ | 153/1376 [00:32<04:57, 4.12it/s]
Model Responding: 11%|█ | 154/1376 [00:33<05:07, 3.98it/s]
Model Responding: 11%|█▏ | 155/1376 [00:33<05:14, 3.88it/s]
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Model Responding: 12%|█▏ | 160/1376 [00:34<05:23, 3.75it/s]
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Model Responding: 12%|█▏ | 164/1376 [00:35<03:45, 5.36it/s]
Model Responding: 12%|█▏ | 165/1376 [00:35<03:53, 5.18it/s]
Model Responding: 12%|█▏ | 166/1376 [00:35<04:22, 4.61it/s]
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Model Responding: 12%|█▏ | 170/1376 [00:36<03:48, 5.27it/s]
Model Responding: 12%|█▏ | 171/1376 [00:36<03:42, 5.42it/s]
Model Responding: 12%|█▎ | 172/1376 [00:37<04:13, 4.75it/s]
Model Responding: 13%|█▎ | 173/1376 [00:37<04:33, 4.39it/s]
Model Responding: 13%|█▎ | 174/1376 [00:37<04:26, 4.52it/s]
Model Responding: 13%|█▎ | 175/1376 [00:37<04:19, 4.62it/s]
Model Responding: 13%|█▎ | 176/1376 [00:37<04:15, 4.69it/s]
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Model Responding: 13%|█▎ | 178/1376 [00:38<04:10, 4.78it/s]
Model Responding: 13%|█▎ | 179/1376 [00:38<04:29, 4.45it/s]
Model Responding: 13%|█▎ | 180/1376 [00:38<04:42, 4.23it/s]
Model Responding: 13%|█▎ | 181/1376 [00:39<04:52, 4.09it/s]
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Model Responding: 13%|█▎ | 185/1376 [00:40<04:27, 4.46it/s]
Model Responding: 14%|█▎ | 186/1376 [00:40<04:21, 4.55it/s]
Model Responding: 14%|█▎ | 187/1376 [00:40<04:17, 4.62it/s]
Model Responding: 14%|█▎ | 188/1376 [00:40<04:14, 4.67it/s]
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Model Responding: 14%|█▍ | 197/1376 [00:42<04:42, 4.17it/s]
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Model Responding: 14%|█▍ | 199/1376 [00:43<04:13, 4.65it/s]
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Model Responding: 15%|█▌ | 210/1376 [00:46<05:23, 3.60it/s]
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Model Responding: 15%|█▌ | 212/1376 [00:46<04:31, 4.29it/s]
Model Responding: 15%|█▌ | 213/1376 [00:46<04:09, 4.66it/s]
Model Responding: 16%|█▌ | 214/1376 [00:47<04:26, 4.36it/s]
Model Responding: 16%|█▌ | 215/1376 [00:47<04:36, 4.19it/s]
Model Responding: 16%|█▌ | 216/1376 [00:47<04:45, 4.07it/s]
Model Responding: 16%|█▌ | 217/1376 [00:48<05:14, 3.69it/s]
Model Responding: 16%|█▌ | 218/1376 [00:48<05:11, 3.72it/s]
Model Responding: 16%|█▌ | 219/1376 [00:48<05:34, 3.46it/s]
Model Responding: 16%|█▌ | 220/1376 [00:48<05:48, 3.32it/s]
Model Responding: 16%|█▌ | 221/1376 [00:49<05:59, 3.21it/s]
Model Responding: 16%|█▌ | 222/1376 [00:49<05:42, 3.37it/s]
Model Responding: 16%|█▌ | 223/1376 [00:49<05:35, 3.44it/s]
Model Responding: 16%|█▋ | 224/1376 [00:50<05:28, 3.50it/s]
Model Responding: 16%|█▋ | 225/1376 [00:50<05:23, 3.56it/s]
Model Responding: 16%|█▋ | 226/1376 [00:50<05:20, 3.59it/s]
Model Responding: 16%|█▋ | 227/1376 [00:50<05:15, 3.64it/s]
Model Responding: 17%|█▋ | 228/1376 [00:51<05:32, 3.45it/s]
Model Responding: 17%|█▋ | 229/1376 [00:51<04:59, 3.83it/s]
Model Responding: 17%|█▋ | 230/1376 [00:51<05:04, 3.76it/s]
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Model Responding: 17%|█▋ | 232/1376 [00:52<05:18, 3.59it/s]
Model Responding: 17%|█▋ | 233/1376 [00:52<05:16, 3.61it/s]
Model Responding: 17%|█▋ | 234/1376 [00:52<04:53, 3.89it/s]
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Model Responding: 17%|█▋ | 236/1376 [00:53<05:14, 3.63it/s]
Model Responding: 17%|█▋ | 237/1376 [00:53<05:21, 3.54it/s]
Model Responding: 17%|█▋ | 238/1376 [00:53<05:14, 3.62it/s]
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Model Responding: 17%|█▋ | 240/1376 [00:54<05:12, 3.64it/s]
Model Responding: 18%|█▊ | 241/1376 [00:54<05:11, 3.65it/s]
Model Responding: 18%|█▊ | 242/1376 [00:55<05:11, 3.64it/s]
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Model Responding: 18%|█▊ | 249/1376 [00:56<04:19, 4.35it/s]
Model Responding: 18%|█▊ | 250/1376 [00:56<04:29, 4.18it/s]
Model Responding: 18%|█▊ | 251/1376 [00:57<04:41, 3.99it/s]
Model Responding: 18%|█▊ | 252/1376 [00:57<04:48, 3.90it/s]
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Model Responding: 19%|█▉ | 268/1376 [01:01<04:22, 4.22it/s]
Model Responding: 20%|█▉ | 269/1376 [01:01<04:29, 4.11it/s]
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Model Responding: 20%|██ | 276/1376 [01:03<05:01, 3.64it/s]
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Model Responding: 20%|██ | 279/1376 [01:04<04:20, 4.21it/s]
Model Responding: 20%|██ | 280/1376 [01:04<03:52, 4.71it/s]
Model Responding: 20%|██ | 281/1376 [01:04<03:32, 5.15it/s]
Model Responding: 20%|██ | 282/1376 [01:04<03:18, 5.51it/s]
Model Responding: 21%|██ | 283/1376 [01:04<03:07, 5.82it/s]
Model Responding: 21%|██ | 284/1376 [01:04<02:59, 6.08it/s]
Model Responding: 21%|██ | 285/1376 [01:04<02:53, 6.27it/s]
Model Responding: 21%|██ | 286/1376 [01:05<02:49, 6.42it/s]
Model Responding: 21%|██ | 287/1376 [01:05<03:00, 6.03it/s]
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TENCENT64:79959:80373 [6] NCCL INFO Channel 00/1 : 6[6] -> 0[0] via P2P/CUMEM/read
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============= MMBench-EN(Dev) Detailed Results =============
Category Acc. (dev):
action_recognition: 88.889
attribute_comparison: 63.636
attribute_recognition: 89.189
celebrity_recognition: 80.808
function_reasoning: 73.418
future_prediction: 42.500
identity_reasoning: 97.778
image_emotion: 80.000
image_quality: 52.830
image_scene: 96.154
image_style: 86.792
image_topic: 77.778
nature_relation: 56.250
object_localization: 46.914
ocr: 66.667
physical_property_reasoning: 49.333
physical_relation: 41.667
social_relation: 93.023
spatial_relationship: 20.000
structuralized_imagetext_understanding: 30.769
L2-category Acc. (dev):
attribute_reasoning: 69.849
coarse_perception: 81.757
finegrained_perception (cross-instance): 59.441
finegrained_perception (instance-level): 71.672
logic_reasoning: 34.746
relation_reasoning: 66.957
[32m2025-05-19 01:50:54.491[0m | [1mINFO [0m | [36men_utils[0m:[36mmmbench_aggregate_dev_results_submission[0m:[36m121[0m - [1mSaved results to /jizhicfs/bojoli/mmpe/mmpe-main/eval/logs/submissions/mmbench_en_dev_results.xlsx[0m
[32m2025-05-19 01:50:55.933[0m | [1mINFO [0m | [36men_utils[0m:[36mmmbench_aggregate_test_results[0m:[36m129[0m - [1mSaved results to /jizhicfs/bojoli/mmpe/mmpe-main/eval/logs/submissions/mmbench_en_test_results.xlsx[0m
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
[32m2025-05-19 01:50:56.096[0m | [1mINFO [0m | [36mlmms_eval.loggers.evaluation_tracker[0m:[36msave_results_aggregated[0m:[36m188[0m - [1mSaving results aggregated[0m
[32m2025-05-19 01:50:56.105[0m | [1mINFO [0m | [36mlmms_eval.loggers.evaluation_tracker[0m:[36msave_results_samples[0m:[36m255[0m - [1mSaving per-sample results for: mmbench_en_dev[0m
[32m2025-05-19 01:50:57.564[0m | [1mINFO [0m | [36mlmms_eval.loggers.evaluation_tracker[0m:[36msave_results_samples[0m:[36m255[0m - [1mSaving per-sample results for: mmbench_en_test[0m
llava (pretrained=../checkpoints/final_mmpe_finetune_vicuna-7b-1.5_clip-vit-large-patch14-336,conv_template=v1), gen_kwargs: (), limit: None, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr|
|------------------|-------|------|-----:|--------------|---|-------|---|------|
|mmbench_en | N/A| | | | | | | |
| - mmbench_en_dev |Yaml |none | 0|gpt_eval_score|↑ |68.2131|± | N/A|
| - mmbench_en_dev |Yaml |none | 0|submission |↑ |N/A |± | N/A|
| - mmbench_en_test|Yaml |none | 0|submission |↑ |N/A |± | N/A|
|