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[run.sh] Launch mode=distributed_sharded (DeepSpeed ZeRO-3)
2026-04-07 13:37:34.662042: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-04-07 13:37:37.839913: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-04-07 13:37:42.467616: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2026-04-07 13:37:42.473892: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.
[train.py] Disabled flash/mem-efficient SDP kernels; using math SDP backend.
[2026-04-07 13:38:10,838][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -fPIC -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -c /scratch/rr81/ma5430/tmp/tmpspb_o7mh/test.c -o /scratch/rr81/ma5430/tmp/tmpspb_o7mh/test.o
[2026-04-07 13:38:24,727][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/g/data/rr81/aev/lib -Wl,-rpath-link,/g/data/rr81/aev/lib -L/g/data/rr81/aev/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib/stubs /scratch/rr81/ma5430/tmp/tmpspb_o7mh/test.o -laio -o /scratch/rr81/ma5430/tmp/tmpspb_o7mh/a.out
[2026-04-07 13:38:25,328][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -fPIC -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -c /scratch/rr81/ma5430/tmp/tmp6efs5r08/test.c -o /scratch/rr81/ma5430/tmp/tmp6efs5r08/test.o
[2026-04-07 13:38:25,385][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/g/data/rr81/aev/lib -Wl,-rpath-link,/g/data/rr81/aev/lib -L/g/data/rr81/aev/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib/stubs /scratch/rr81/ma5430/tmp/tmp6efs5r08/test.o -L/g/data/rr81/aev -L/g/data/rr81/aev/lib64 -lcufile -o /scratch/rr81/ma5430/tmp/tmp6efs5r08/a.out
[2026-04-07 13:38:25,551][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -DNDEBUG -D_FORTIFY_SOURCE=2 -O2 -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -fPIC -march=nocona -mtune=haswell -ftree-vectorize -fPIC -fstack-protector-strong -fno-plt -O2 -ffunction-sections -pipe -isystem /g/data/rr81/aev/include -I/g/data/rr81/aev/targets/x86_64-linux/include -I/g/data/rr81/aev/targets/x86_64-linux/include/cccl -c /scratch/rr81/ma5430/tmp/tmp0ddzq53f/test.c -o /scratch/rr81/ma5430/tmp/tmp0ddzq53f/test.o
[2026-04-07 13:38:25,609][root][INFO] - /g/data/rr81/aev/bin/x86_64-conda-linux-gnu-cc -Wl,-O2 -Wl,--sort-common -Wl,--as-needed -Wl,-z,relro -Wl,-z,now -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/g/data/rr81/aev/lib -Wl,-rpath-link,/g/data/rr81/aev/lib -L/g/data/rr81/aev/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib -L/g/data/rr81/aev/targets/x86_64-linux/lib/stubs /scratch/rr81/ma5430/tmp/tmp0ddzq53f/test.o -laio -o /scratch/rr81/ma5430/tmp/tmp0ddzq53f/a.out
[2026-04-07 13:38:28,753][accelerate.utils.other][WARNING] - Detected kernel version 4.18.0, 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.
[2026-04-07 13:38:28,754][trainer.accelerators.base_accelerator][INFO] - Setting seed 42
[2026-04-07 13:38:28,771][trainer.accelerators.base_accelerator][INFO] - Initialized accelerator: rank=0
[2026-04-07 13:38:28,777][__main__][INFO] - Config can be found in logs/v5/reward_model/step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951/config.yaml
[2026-04-07 13:38:28,778][__main__][INFO] - Loading task
[2026-04-07 13:38:29,730][__main__][INFO] - Loading model
`torch_dtype` is deprecated! Use `dtype` instead!
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[2026-04-07 13:38:33,300][__main__][INFO] - Loading criterion
[2026-04-07 13:38:33,301][__main__][INFO] - Loading optimizer
[2026-04-07 13:38:33,305][__main__][INFO] - Loading lr scheduler
[2026-04-07 13:38:33,306][__main__][INFO] - Loading dataloaders
[2026-04-07 13:38:33,307][trainer.datasets.step_sana_hf_dataset][INFO] - Using step-aware datasets
[2026-04-07 13:38:33,307][trainer.datasets.step_sana_hf_dataset][INFO] - Loading train dataset
[2026-04-07 13:38:33,307][trainer.datasets.step_sana_hf_dataset][INFO] - Batch size is 4
[2026-04-07 13:38:33,309][trainer.datasets.step_sana_hf_dataset][INFO] - Loading cached offline split 'train' from 387 parquet shards
[2026-04-07 13:38:34,488][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 583747 examples from train dataset
[2026-04-07 13:38:35,043][trainer.datasets.step_sana_hf_dataset][INFO] - Keeping only examples with pesudo preference, filter_strategy: 2
[2026-04-07 13:38:35,050][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 583747 examples from train dataset
[2026-04-07 13:38:35,241][trainer.datasets.step_sana_hf_dataset][INFO] - Kept 177076 examples from train dataset
[2026-04-07 13:38:35,242][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 177076 examples from train dataset
[2026-04-07 13:38:36,181][trainer.datasets.step_sana_hf_dataset][INFO] - Using step-aware datasets
[2026-04-07 13:38:36,181][trainer.datasets.step_sana_hf_dataset][INFO] - Loading validation_unique dataset
[2026-04-07 13:38:36,181][trainer.datasets.step_sana_hf_dataset][INFO] - Batch size is 4
[2026-04-07 13:38:36,182][trainer.datasets.step_sana_hf_dataset][INFO] - Loading cached offline split 'validation_unique' from 1 parquet shards
[2026-04-07 13:38:36,193][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 500 examples from validation_unique dataset
[2026-04-07 13:38:36,194][trainer.datasets.step_sana_hf_dataset][INFO] - Keeping only examples with label in validation_unique split
Filter: 0%| | 0/500 [00:00<?, ? examples/s]
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Filter: 100%|██████████| 500/500 [00:00<00:00, 3530.07 examples/s]
[2026-04-07 13:38:36,359][trainer.datasets.step_sana_hf_dataset][INFO] - Kept 425 examples from validation_unique dataset
[2026-04-07 13:38:36,360][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 425 examples from validation_unique dataset
[2026-04-07 13:38:37,357][trainer.datasets.step_sana_hf_dataset][INFO] - Using step-aware datasets
[2026-04-07 13:38:37,357][trainer.datasets.step_sana_hf_dataset][INFO] - Loading test_unique dataset
[2026-04-07 13:38:37,357][trainer.datasets.step_sana_hf_dataset][INFO] - Batch size is 4
[2026-04-07 13:38:37,358][trainer.datasets.step_sana_hf_dataset][INFO] - Loading cached offline split 'test_unique' from 1 parquet shards
[2026-04-07 13:38:37,369][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 500 examples from test_unique dataset
[2026-04-07 13:38:37,369][trainer.datasets.step_sana_hf_dataset][INFO] - Keeping only examples with label in test_unique split
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Filter: 100%|██████████| 500/500 [00:00<00:00, 3311.47 examples/s]
[2026-04-07 13:38:37,545][trainer.datasets.step_sana_hf_dataset][INFO] - Kept 432 examples from test_unique dataset
[2026-04-07 13:38:37,545][trainer.datasets.step_sana_hf_dataset][INFO] - Loaded 432 examples from test_unique dataset
[2026-04-07 13:38:38,482][accelerate.accelerator][INFO] - Since you passed both train and evaluation dataloader, `is_train_batch_min` (here True will decide the `train_batch_size` (4).
[2026-04-07 13:38:44,136] [WARNING] [lr_schedules.py:690:get_lr] Attempting to get learning rate from scheduler before it has started
[2026-04-07 13:38:44,144][trainer.accelerators.base_accelerator][INFO] - num_update_steps_per_epoch = 44269
[2026-04-07 13:38:44,144][trainer.accelerators.base_accelerator][INFO] - num_batches = 44269
[2026-04-07 13:38:44,144][trainer.accelerators.base_accelerator][INFO] - num_epochs = 1
[2026-04-07 13:38:44,182][trainer.accelerators.base_accelerator][INFO] - Initializing trackers
[2026-04-07 13:38:44,182][trainer.accelerators.base_accelerator][INFO] - Training config:
CONFIG
├── accelerator
│ └── _target_: trainer.accelerators.deepspeed_accelerator.DeepSpeedAccelerator
│ output_dir: logs/v5/reward_model/step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951
│ mixed_precision: BF16
│ gradient_accumulation_steps: 1
│ log_with: null
│ debug:
│ activate: false
│ port: 5900
│ seed: 42
│ resume_from_checkpoint: false
│ max_steps: 8000
│ num_epochs: 1
│ validate_steps: 100
│ generalization_validate_steps: 500
│ eval_on_start: false
│ project_name: reward_model
│ run_name: step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951
│ max_grad_norm: 1.0
│ save_steps: 100
│ metric_name: accuracy
│ metric_mode: MAX
│ limit_num_checkpoints: 1
│ save_only_if_best: true
│ dynamo_backend: 'NO'
│ keep_best_ckpts: true
│ progress_log_interval: 50
│ deepspeed:
│ fp16:
│ enabled: false
│ bf16:
│ enabled: true
│ optimizer:
│ type: AdamW
│ params:
│ lr: auto
│ weight_decay: auto
│ torch_adam: true
│ adam_w_mode: true
│ scheduler:
│ type: WarmupDecayLR
│ params:
│ warmup_min_lr: auto
│ warmup_max_lr: auto
│ warmup_num_steps: auto
│ total_num_steps: auto
│ zero_optimization:
│ stage: 3
│ allgather_partitions: true
│ allgather_bucket_size: 200000000.0
│ overlap_comm: true
│ reduce_scatter: true
│ reduce_bucket_size: 500000000
│ contiguous_gradients: true
│ gradient_accumulation_steps: 1
│ gradient_clipping: 1.0
│ steps_per_print: 1
│ train_batch_size: auto
│ train_micro_batch_size_per_gpu: auto
│ wall_clock_breakdown: false
│ deepspeed_final:
│ fp16:
│ enabled: false
│ bf16:
│ enabled: true
│ optimizer:
│ type: AdamW
│ params:
│ lr: auto
│ weight_decay: auto
│ torch_adam: true
│ adam_w_mode: true
│ scheduler:
│ type: WarmupDecayLR
│ params:
│ warmup_min_lr: auto
│ warmup_max_lr: auto
│ warmup_num_steps: auto
│ total_num_steps: auto
│ zero_optimization:
│ stage: 3
│ allgather_partitions: true
│ allgather_bucket_size: 200000000.0
│ overlap_comm: true
│ reduce_scatter: true
│ reduce_bucket_size: 500000000
│ contiguous_gradients: true
│ gradient_accumulation_steps: 1
│ gradient_clipping: 1.0
│ steps_per_print: .inf
│ train_batch_size: auto
│ train_micro_batch_size_per_gpu: auto
│ wall_clock_breakdown: false
│
├── task
│ └── limit_examples_to_wandb: 50
│ _target_: trainer.tasks.step_sana_task.StepSanaTask
│ pretrained_model_name_or_path: Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers
│ tokenizer_subfolder: tokenizer
│ label_0_column_name: label_0
│ label_1_column_name: label_1
│ input_ids_column_name: input_ids
│ input_ids_2_column_name: input_ids_2
│ pixels_0_column_name: pixel_values_0
│ pixels_1_column_name: pixel_values_1
│ timestep_column_name: timestep
│ constant_timestep: 1
│
├── model
│ └── _target_: trainer.models.sana_preference_model.SanaPreferenceModel
│ pretrained_model_name_or_path: Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers
│ pretrained_vae_name_or_path: ''
│ model_profile: sana_sprint_0_6b_1024
│ projection_dim: 1024
│ logit_scale_init_value: 2.6592
│ freeze_text_encoder: false
│ guidance_scale: 7.5
│ noise_offset: false
│ noise_offset_coeff: 0.05
│ max_sequence_length: 300
│ max_sequence_length_2: 300
│ image_size: 1024
│
├── criterion
│ └── _target_: trainer.criterions.step_clip_criterion_sana.StepSanaCLIPCriterion
│ is_distributed: true
│ label_0_column_name: label_0
│ label_1_column_name: label_1
│ input_ids_column_name: input_ids
│ input_ids_2_column_name: input_ids_2
│ pixels_0_column_name: pixel_values_0
│ pixels_1_column_name: pixel_values_1
│ num_examples_per_prompt_column_name: num_example_per_prompt
│ timestep_column_name: timestep
│ loss_type: pair
│ batch_coeff: 1.0
│ aux_loss_coeff: 1.0
│
├── dataset
│ └── train_split_name: train
│ valid_split_name: validation_unique
│ test_split_name: test_unique
│ batch_size: 4
│ num_workers: 2
│ drop_last: true
│ _target_: trainer.datasets.step_sana_hf_dataset.StepSanaHFDataset
│ dataset_name: pickapic-anonymous/pickapic_v1
│ dataset_config_name: null
│ from_disk: false
│ cache_dir: null
│ caption_column_name: caption
│ input_ids_column_name: input_ids
│ input_ids_2_column_name: input_ids_2
│ image_0_column_name: jpg_0
│ image_1_column_name: jpg_1
│ label_0_column_name: label_0
│ label_1_column_name: label_1
│ are_different_column_name: are_different
│ has_label_column_name: has_label
│ pixels_0_column_name: pixel_values_0
│ pixels_1_column_name: pixel_values_1
│ timestep_column_name: timestep
│ constant_timestep: 1
│ variable_timestep: true
│ largest_timestep: 951
│ compare_between_timestep: false
│ timestep_comparison_column_name: timestep_comparison
│ timestep_interval: 1
│ num_examples_per_prompt_column_name: num_example_per_prompt
│ keep_only_different: false
│ keep_only_with_label: false
│ keep_only_with_label_in_non_train: true
│ keep_only_with_pesudo_preference: true
│ pseudo_preference_path: /g/data/rr81/LPO/lrm/lrm_sana/vqa_aes_clip_score_mp.csv
│ filter_strategy: 2
│ processor:
│ pretrained_model_name_or_path: Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers
│ max_sequence_length: 300
│ max_sequence_length_2: 300
│ image_size: 1024
│ random_crop: false
│ no_hflip: true
│ limit_examples_per_prompt: -1
│ only_on_best: false
│
├── optimizer
│ └── _target_: trainer.optimizers.dummy_optimizer.BaseDummyOptim
│ lr: 1.0e-05
│ weight_decay: 0.3
│
├── lr_scheduler
│ └── _target_: trainer.lr_schedulers.dummy_lr_scheduler.instantiate_dummy_lr_scheduler
│ lr: 1.0e-05
│ lr_warmup_steps: 1000
│ total_num_steps: 8000
│
├── debug
│ └── activate: false
│ port: 5900
│
└── output_dir
└── logs/v5/reward_model/step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951
[2026-04-07 13:38:44,256][trainer.accelerators.base_accelerator][INFO] - nvidia-smi stats: {'gpu_0_mem_used_gb': 35.8818359375}
[2026-04-07 13:38:44,256][trainer.accelerators.base_accelerator][INFO] - ***** Running training *****
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Instantaneous batch size per device = 4
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Total train batch size (w. parallel, distributed & accumulation) = 4
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Gradient Accumulation steps = 1
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Total warmup steps = 1000
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Total training steps = 8000
[2026-04-07 13:38:44,257][trainer.accelerators.base_accelerator][INFO] - Total epochs = 1
[2026-04-07 13:38:44,258][trainer.accelerators.base_accelerator][INFO] - Steps per epoch = 44269
[2026-04-07 13:38:44,258][trainer.accelerators.base_accelerator][INFO] - Update steps per epoch = 44269
[2026-04-07 13:38:44,258][trainer.accelerators.base_accelerator][INFO] - Total optimization steps = 8000
[2026-04-07 13:38:44,258][trainer.accelerators.base_accelerator][INFO] - Mixed precision = bf16
[2026-04-07 13:38:44,258][trainer.accelerators.base_accelerator][INFO] - World size = 1
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Steps: 0%| | 0/8000 [00:00<?, ?it/s][2026-04-07 13:38:44,260][__main__][INFO] - task: StepSanaTask
[2026-04-07 13:38:44,260][__main__][INFO] - model: DeepSpeedEngine
[2026-04-07 13:38:44,269][__main__][INFO] - num. model params: 0M
[2026-04-07 13:38:44,278][__main__][INFO] - num. model trainable params: 0M
[2026-04-07 13:38:44,278][__main__][INFO] - criterion: StepSanaCLIPCriterion
[2026-04-07 13:38:44,278][__main__][INFO] - num. train examples: 177076
[2026-04-07 13:38:44,279][__main__][INFO] - num. valid examples: 425
[2026-04-07 13:38:44,279][__main__][INFO] - num. test examples: 432
[2026-04-07 13:38:44,279][__main__][INFO] - ========== TRAIN LOOP START (eval_on_start=False, validate_steps=100, progress_log_interval=50) ==========
[2026-04-07 13:38:45,177] [WARNING] [torch_autocast.py:122:autocast_if_enabled] torch.autocast is enabled outside DeepSpeed but disabled within the DeepSpeed engine. If you are using DeepSpeed's built-in mixed precision, the engine will follow the settings in bf16/fp16 section. To use torch's native autocast instead, configure the `torch_autocast` section in the DeepSpeed config.
Error executing job with overrides: ['accelerator.mixed_precision=BF16', 'model.model_profile=sana_sprint_0_6b_1024', 'model.pretrained_model_name_or_path=Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers', 'model.image_size=1024', 'accelerator.run_name=step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951', 'accelerator.log_with=null', 'accelerator=deepspeed', 'optimizer=dummy', 'lr_scheduler=dummy', 'criterion.is_distributed=true', 'accelerator.deepspeed.zero_optimization.stage=3', 'accelerator.deepspeed.gradient_accumulation_steps=1', 'dataset.pseudo_preference_path=/g/data/rr81/LPO/lrm/lrm_sana/vqa_aes_clip_score_mp.csv', 'dataset.valid_split_name=validation_unique', 'dataset.test_split_name=test_unique']
Traceback (most recent call last):
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/scripts/train.py", line 201, in main
loss = task.train_step(model, criterion, batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/tasks/step_sana_task.py", line 53, in train_step
loss = criterion(model, batch)
^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/criterions/step_clip_criterion_sana.py", line 217, in forward
image_0_features, image_1_features, text_features = self.get_features(
^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/criterions/step_clip_criterion_sana.py", line 35, in get_features
text_features, all_image_features = model(text_input_ids=input_ids, text_input_ids_2=input_ids_2, image_inputs=all_pixel_values, time_cond=timesteps)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1786, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/amp/autocast_mode.py", line 44, in decorate_autocast
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
ret_val = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/runtime/engine.py", line 2358, in forward
loss = self.module(*inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1881, in _call_impl
return inner()
^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1829, in inner
result = forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/models/sana_preference_model.py", line 310, in forward
image_features = self.get_image_features(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/LPO/lrm/lrm_sana/trainer/models/sana_preference_model.py", line 288, in get_image_features
model_pred = self.transformer(**transformer_kwargs)[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1881, in _call_impl
return inner()
^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1829, in inner
result = forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/diffusers/models/transformers/sana_transformer.py", line 506, in forward
hidden_states = block(
^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1881, in _call_impl
return inner()
^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1829, in inner
result = forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/diffusers/models/transformers/sana_transformer.py", line 273, in forward
attn_output = self.attn2(
^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1881, in _call_impl
return inner()
^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1829, in inner
result = forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/diffusers/models/attention_processor.py", line 605, in forward
return self.processor(
^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/diffusers/models/transformers/sana_transformer.py", line 179, in __call__
hidden_states = attn.to_out[0](hidden_states)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1775, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1881, in _call_impl
return inner()
^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1818, in inner
args_result = hook(self, args)
^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 1044, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/runtime/zero/parameter_offload.py", line 300, in _pre_forward_module_hook
self.pre_sub_module_forward_function(module)
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/runtime/zero/parameter_offload.py", line 475, in pre_sub_module_forward_function
param_coordinator.fetch_sub_module(sub_module, forward=True)
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 1044, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/utils/nvtx.py", line 20, in wrapped_fn
ret_val = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/runtime/zero/partitioned_param_coordinator.py", line 325, in fetch_sub_module
self._fetch_sub_module_impl(current_submodule, forward, is_leaf)
File "/g/data/rr81/aev/lib/python3.11/site-packages/deepspeed/runtime/zero/partitioned_param_coordinator.py", line 377, in _fetch_sub_module_impl
while self.__ongoing_fetch_events and self.__ongoing_fetch_events[0].query():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/torch/cuda/streams.py", line 212, in query
return super().query()
^^^^^^^^^^^^^^^
torch.AcceleratorError: CUDA error: misaligned address
Search for `cudaErrorMisalignedAddress' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
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Traceback (most recent call last):
File "/g/data/rr81/aev/bin/accelerate", line 7, in <module>
sys.exit(main())
^^^^^^
File "/g/data/rr81/aev/lib/python3.11/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
args.func(args)
File "/g/data/rr81/aev/lib/python3.11/site-packages/accelerate/commands/launch.py", line 1281, in launch_command
simple_launcher(args)
File "/g/data/rr81/aev/lib/python3.11/site-packages/accelerate/commands/launch.py", line 869, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/g/data/rr81/aev/bin/python3.11', 'trainer/scripts/train.py', '--config-path', '/g/data/rr81/LPO/lrm/lrm_sana/trainer/conf', '--config-name', 'step_sana_base', 'accelerator.mixed_precision=BF16', 'model.model_profile=sana_sprint_0_6b_1024', 'model.pretrained_model_name_or_path=Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers', 'model.image_size=1024', 'accelerator.run_name=step_sana_sana_sprint_0_6b_1024_variable-t_lr1e-5_step-8000_filter2_time951', 'accelerator.log_with=null', 'accelerator=deepspeed', 'optimizer=dummy', 'lr_scheduler=dummy', 'criterion.is_distributed=true', 'accelerator.deepspeed.zero_optimization.stage=3', 'accelerator.deepspeed.gradient_accumulation_steps=1', 'dataset.pseudo_preference_path=/g/data/rr81/LPO/lrm/lrm_sana/vqa_aes_clip_score_mp.csv', 'dataset.valid_split_name=validation_unique', 'dataset.test_split_name=test_unique']' returned non-zero exit status 1.
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