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MASTER_ADDR: c31
SLURM_CPUS_PER_TASK: 64
Tue Oct 21 16:43:16 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.82.07 Driver Version: 580.82.07 CUDA Version: 13.0 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA RTX A6000 On | 00000000:01:00.0 Off | Off |
| 30% 31C P8 23W / 200W | 1MiB / 49140MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA RTX A6000 On | 00000000:41:00.0 Off | Off |
| 30% 32C P8 23W / 200W | 1MiB / 49140MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA RTX A6000 On | 00000000:81:00.0 Off | Off |
| 30% 32C P8 15W / 200W | 1MiB / 49140MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 3 NVIDIA RTX A6000 On | 00000000:C1:00.0 Off | Off |
| 30% 32C P8 18W / 200W | 1MiB / 49140MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
[2025-10-21 16:43:29,081] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Warning: The cache directory for DeepSpeed Triton autotune, /home/dzhu6/.triton/autotune, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path.
[INFO|2025-10-21 16:43:31] llamafactory.launcher:143 >> Initializing 4 distributed tasks at: c31:12760
W1021 16:43:34.509000 1250401 site-packages/torch/distributed/run.py:792]
W1021 16:43:34.509000 1250401 site-packages/torch/distributed/run.py:792] *****************************************
W1021 16:43:34.509000 1250401 site-packages/torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W1021 16:43:34.509000 1250401 site-packages/torch/distributed/run.py:792] *****************************************
[2025-10-21 16:43:43,932] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Warning: The cache directory for DeepSpeed Triton autotune, /home/dzhu6/.triton/autotune, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path.
[2025-10-21 16:43:44,234] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-21 16:43:44,244] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2025-10-21 16:43:44,449] [INFO] [real_accelerator.py:254:get_accelerator] Setting ds_accelerator to cuda (auto detect)
Warning: The cache directory for DeepSpeed Triton autotune, /home/dzhu6/.triton/autotune, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path.
Warning: The cache directory for DeepSpeed Triton autotune, /home/dzhu6/.triton/autotune, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path.
Warning: The cache directory for DeepSpeed Triton autotune, /home/dzhu6/.triton/autotune, appears to be on an NFS system. While this is generally acceptable, if you experience slowdowns or hanging when DeepSpeed exits, it is recommended to set the TRITON_CACHE_DIR environment variable to a non-NFS path.
[2025-10-21 16:43:50,925] [INFO] [comm.py:669:init_distributed] cdb=None
[2025-10-21 16:43:50,926] [INFO] [comm.py:669:init_distributed] cdb=None
[2025-10-21 16:43:50,939] [INFO] [comm.py:669:init_distributed] cdb=None
[2025-10-21 16:43:51,096] [INFO] [comm.py:669:init_distributed] cdb=None
[2025-10-21 16:43:51,097] [INFO] [comm.py:700:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
[INFO|2025-10-21 16:43:51] llamafactory.hparams.parser:423 >> Process rank: 0, world size: 4, device: cuda:0, distributed training: True, compute dtype: torch.bfloat16
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,231 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,231 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,232 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,232 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,232 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,233 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,233 >> loading file chat_template.jinja
[INFO|2025-10-21 16:43:51] llamafactory.hparams.parser:423 >> Process rank: 3, world size: 4, device: cuda:3, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-21 16:43:51] llamafactory.hparams.parser:423 >> Process rank: 2, world size: 4, device: cuda:2, distributed training: True, compute dtype: torch.bfloat16
[INFO|2025-10-21 16:43:51] llamafactory.hparams.parser:423 >> Process rank: 1, world size: 4, device: cuda:1, distributed training: True, compute dtype: torch.bfloat16
[INFO|tokenization_utils_base.py:2323] 2025-10-21 16:43:51,429 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|image_processing_base.py:378] 2025-10-21 16:43:51,432 >> loading configuration file /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[INFO|image_processing_base.py:378] 2025-10-21 16:43:51,436 >> loading configuration file /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct/preprocessor_config.json
[INFO|image_processing_base.py:433] 2025-10-21 16:43:51,440 >> Image processor Qwen2VLImageProcessorFast {
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessorFast",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,443 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,443 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,444 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,444 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,444 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,445 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2058] 2025-10-21 16:43:51,445 >> loading file chat_template.jinja
[INFO|tokenization_utils_base.py:2323] 2025-10-21 16:43:51,630 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|processing_utils.py:884] 2025-10-21 16:43:51,941 >> Processor Qwen2_5_VLProcessor:
- image_processor: Qwen2VLImageProcessorFast {
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessorFast",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_tensors": null,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2
}
- tokenizer: Qwen2TokenizerFast(name_or_path='/mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct', vocab_size=151643, model_max_length=131072, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'eos_token': '<|im_end|>', 'pad_token': '<|endoftext|>', 'additional_special_tokens': ['<|im_start|>', '<|im_end|>', '<|object_ref_start|>', '<|object_ref_end|>', '<|box_start|>', '<|box_end|>', '<|quad_start|>', '<|quad_end|>', '<|vision_start|>', '<|vision_end|>', '<|vision_pad|>', '<|image_pad|>', '<|video_pad|>']}, clean_up_tokenization_spaces=False, added_tokens_decoder={
151643: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151644: AddedToken("<|im_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151645: AddedToken("<|im_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151646: AddedToken("<|object_ref_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151647: AddedToken("<|object_ref_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151648: AddedToken("<|box_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151649: AddedToken("<|box_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151650: AddedToken("<|quad_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151651: AddedToken("<|quad_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151652: AddedToken("<|vision_start|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151653: AddedToken("<|vision_end|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151654: AddedToken("<|vision_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151655: AddedToken("<|image_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151656: AddedToken("<|video_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
151657: AddedToken("<tool_call>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151658: AddedToken("</tool_call>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151659: AddedToken("<|fim_prefix|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151660: AddedToken("<|fim_middle|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151661: AddedToken("<|fim_suffix|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151662: AddedToken("<|fim_pad|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151663: AddedToken("<|repo_name|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
151664: AddedToken("<|file_sep|>", rstrip=False, lstrip=False, single_word=False, normalized=False, special=False),
}
)
{
"processor_class": "Qwen2_5_VLProcessor"
}
[INFO|2025-10-21 16:43:51] llamafactory.data.loader:143 >> Loading dataset cold_start/vqa_filtered.json...
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/vqa_filtered.json']
[rank3]:[W1021 16:43:52.482298275 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 3] using GPU 3 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
[rank1]:[W1021 16:43:52.514515771 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 1] using GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
[rank2]:[W1021 16:43:52.517760380 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 2] using GPU 2 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
Converting format of dataset (num_proc=64): 0%| | 0/10000 [00:00<?, ? examples/s][WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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Converting format of dataset (num_proc=64): 5%|▍ | 479/10000 [00:00<00:02, 4698.81 examples/s][WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:52] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 54%|█████▎ | 5369/10000 [00:00<00:00, 30145.20 examples/s]
Converting format of dataset (num_proc=64): 93%|█████████▎| 9283/10000 [00:00<00:00, 34055.88 examples/s]
Converting format of dataset (num_proc=64): 100%|██████████| 10000/10000 [00:00<00:00, 24244.46 examples/s]
[INFO|2025-10-21 16:43:53] llamafactory.data.loader:143 >> Loading dataset cold_start/maze_filtered.json...
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/maze_filtered.json']
Converting format of dataset (num_proc=64): 0%| | 0/7298 [00:00<?, ? examples/s][WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 11%|█▏ | 832/7298 [00:00<00:00, 7976.50 examples/s][WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:54] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 57%|█████▋ | 4196/7298 [00:00<00:00, 22771.06 examples/s]
Converting format of dataset (num_proc=64): 89%|████████▉ | 6511/7298 [00:00<00:00, 20827.02 examples/s]
Converting format of dataset (num_proc=64): 100%|██████████| 7298/7298 [00:00<00:00, 16480.23 examples/s]
[INFO|2025-10-21 16:43:54] llamafactory.data.loader:143 >> Loading dataset cold_start/GPT4Scene_filtered.json...
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/GPT4Scene_filtered.json']
Converting format of dataset (num_proc=64): 0%| | 0/4930 [00:00<?, ? examples/s][WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 10%|█ | 504/4930 [00:00<00:00, 4962.60 examples/s][WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:55] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 50%|████▉ | 2450/4930 [00:00<00:00, 13399.90 examples/s]
Converting format of dataset (num_proc=64): 83%|████████▎ | 4080/4930 [00:00<00:00, 14645.99 examples/s]
Converting format of dataset (num_proc=64): 100%|██████████| 4930/4930 [00:00<00:00, 10387.37 examples/s]
[INFO|2025-10-21 16:43:55] llamafactory.data.loader:143 >> Loading dataset cold_start/SR_91k_filtered.json...
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/SR_91k_filtered.json']
Converting format of dataset (num_proc=64): 0%| | 0/11352 [00:00<?, ? examples/s][WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
Converting format of dataset (num_proc=64): 4%|▍ | 476/11352 [00:00<00:02, 4720.90 examples/s][WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
[WARNING|2025-10-21 16:43:56] llamafactory.data.converter:154 >> dataset_attr.load_from: file
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Converting format of dataset (num_proc=64): 22%|██▏ | 2445/11352 [00:00<00:00, 13411.89 examples/s]
Converting format of dataset (num_proc=64): 39%|███▉ | 4452/11352 [00:00<00:00, 16332.04 examples/s]
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Converting format of dataset (num_proc=64): 90%|█████████ | 10237/11352 [00:00<00:00, 18154.04 examples/s]
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[rank0]:[W1021 16:43:57.237527457 ProcessGroupNCCL.cpp:4561] [PG ID 0 PG GUID 0 Rank 0] using GPU 0 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect. Specify device_ids in barrier() to force use of a particular device, or call init_process_group() with a device_id.
dataset: Dataset({
features: ['_prompt', '_response', '_system', '_tools', '_images', '_videos', '_audios'],
num_rows: 33580
})data_path:
data_path:data_path: json json{'_prompt': [{'content': "<image>\n### Question:\nAre they touching feet?\nA) No\nB) Yes\nThe index of the given image is 1.\nAnswer with the option's letter from the given choices directly.\n\nBegin your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n", 'role': 'user'}, {'content': '<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.\n</think> Action: Object Mapper\n```json\n[{\n "index": 1,\n "bbox_2d": [124, 49, 389, 336],\n "label": "man holding remote"\n},\n{\n "index": 1,\n "bbox_2d": [201, 143, 286, 292],\n "label": "boy sitting in chair"\n},\n{\n "index": 1,\n "bbox_2d": [419, 155, 504, 336],\n "label": "woman sitting on couch"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 2 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}, {'content': '<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.\n</think> Action: Path Tracer\n```json\n[{\n "index": 2,\n "start_point_2d": [335, 320],\n "end_point_2d": [232, 280],\n "label": "feet of man and boy"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 3 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}], '_response': [{'content': "<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.\n</think> Action: Answer\n<answer> A </answer>", 'role': 'assistant'}], '_system': 'system\n### Guidance:\nYou are a spatial reasoning assistant with access to two powerful visualization tools.\nYour task is to break down complex spatial problems and iteratively refine your solution through visualization feedback.\n\n### Available tools:\nYou can use the following two tools to visualize. After each tool usage, you must wait for and analyze the visualization feedback before proceeding.\n\n1. **Object Mapper**\n- Purpose: Identifies and maps key items in the space\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "bbox_2d": [x1, y1, x2, y2],\n "label": "object name/description"\n}]\n```\n- Output: Generates bounding boxes for visual inspection of the i-th image\n\n2. **Path Tracer**\n- Purpose: Plots movement or connections between points\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "start_point_2d": [x1, y1],\n "end_point_2d": [x2, y2],\n "label": "trace_description"\n}]\n```\n- Output: Generates visual paths for verification of the i-th image\n\n### Required Output Format:\nFor each reasoning step, you must structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: [Object Mapper/Path Tracer]\n```json\n[JSON format coordinates]\n```\n\nAfter your reasoning and iteratively refine your solution through visualization feedback, you should arrive at a final answer and structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: Answer\n<answer> [Your final answer] </answer>\n\n### Please NOTE the following reasoning techniques:\n1. Initial Analysis\n - Break down the spatial problem\n - Plan your approach\n\n2. Iterative Reasoning for Each Step\n - Choose appropriate tool\n - Provide absolute coordinates in JSON format (The top-left corner of the image is (0, 0) and the bottom-right corner is (504, 336))\n - Observe the visualization output\n - Reflect on the visualization:\n * Is the placement/path accurate?\n * Does it align with your reasoning?\n * What adjustments are needed?\n - Backtrack and Adjust:\n * If errors found, backtrack to previous step to modify actions or decisions as needed', '_tools': '', '_images': ['/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_0.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_1.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_2.png'], '_videos': None, '_audios': None}
data_name:json
data_name:
Nonedata_name: None
data_dir:None
data_dir: Nonedata_dir: None
data_files:None
data_files: data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/vqa_filtered.json']['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/vqa_filtered.json']
['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/vqa_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/maze_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/maze_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/maze_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/GPT4Scene_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/GPT4Scene_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/GPT4Scene_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/SR_91k_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/SR_91k_filtered.json']
data_path: json
data_name: None
data_dir: None
data_files: ['/mnt/beegfs/dzhu6/ViLaSR-data/cold_start/SR_91k_filtered.json']
Running tokenizer on dataset (num_proc=64): 0%| | 0/33580 [00:00<?, ? examples/s]dataset: Dataset({
features: ['_prompt', '_response', '_system', '_tools', '_images', '_videos', '_audios'],
num_rows: 33580
})
{'_prompt': [{'content': "<image>\n### Question:\nAre they touching feet?\nA) No\nB) Yes\nThe index of the given image is 1.\nAnswer with the option's letter from the given choices directly.\n\nBegin your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n", 'role': 'user'}, {'content': '<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.\n</think> Action: Object Mapper\n```json\n[{\n "index": 1,\n "bbox_2d": [124, 49, 389, 336],\n "label": "man holding remote"\n},\n{\n "index": 1,\n "bbox_2d": [201, 143, 286, 292],\n "label": "boy sitting in chair"\n},\n{\n "index": 1,\n "bbox_2d": [419, 155, 504, 336],\n "label": "woman sitting on couch"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 2 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}, {'content': '<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.\n</think> Action: Path Tracer\n```json\n[{\n "index": 2,\n "start_point_2d": [335, 320],\n "end_point_2d": [232, 280],\n "label": "feet of man and boy"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 3 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}], '_response': [{'content': "<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.\n</think> Action: Answer\n<answer> A </answer>", 'role': 'assistant'}], '_system': 'system\n### Guidance:\nYou are a spatial reasoning assistant with access to two powerful visualization tools.\nYour task is to break down complex spatial problems and iteratively refine your solution through visualization feedback.\n\n### Available tools:\nYou can use the following two tools to visualize. After each tool usage, you must wait for and analyze the visualization feedback before proceeding.\n\n1. **Object Mapper**\n- Purpose: Identifies and maps key items in the space\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "bbox_2d": [x1, y1, x2, y2],\n "label": "object name/description"\n}]\n```\n- Output: Generates bounding boxes for visual inspection of the i-th image\n\n2. **Path Tracer**\n- Purpose: Plots movement or connections between points\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "start_point_2d": [x1, y1],\n "end_point_2d": [x2, y2],\n "label": "trace_description"\n}]\n```\n- Output: Generates visual paths for verification of the i-th image\n\n### Required Output Format:\nFor each reasoning step, you must structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: [Object Mapper/Path Tracer]\n```json\n[JSON format coordinates]\n```\n\nAfter your reasoning and iteratively refine your solution through visualization feedback, you should arrive at a final answer and structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: Answer\n<answer> [Your final answer] </answer>\n\n### Please NOTE the following reasoning techniques:\n1. Initial Analysis\n - Break down the spatial problem\n - Plan your approach\n\n2. Iterative Reasoning for Each Step\n - Choose appropriate tool\n - Provide absolute coordinates in JSON format (The top-left corner of the image is (0, 0) and the bottom-right corner is (504, 336))\n - Observe the visualization output\n - Reflect on the visualization:\n * Is the placement/path accurate?\n * Does it align with your reasoning?\n * What adjustments are needed?\n - Backtrack and Adjust:\n * If errors found, backtrack to previous step to modify actions or decisions as needed', '_tools': '', '_images': ['/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_0.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_1.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_2.png'], '_videos': None, '_audios': None}
dataset: Dataset({
features: ['_prompt', '_response', '_system', '_tools', '_images', '_videos', '_audios'],
num_rows: 33580
})
dataset:{'_prompt': [{'content': "<image>\n### Question:\nAre they touching feet?\nA) No\nB) Yes\nThe index of the given image is 1.\nAnswer with the option's letter from the given choices directly.\n\nBegin your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n", 'role': 'user'}, {'content': '<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.\n</think> Action: Object Mapper\n```json\n[{\n "index": 1,\n "bbox_2d": [124, 49, 389, 336],\n "label": "man holding remote"\n},\n{\n "index": 1,\n "bbox_2d": [201, 143, 286, 292],\n "label": "boy sitting in chair"\n},\n{\n "index": 1,\n "bbox_2d": [419, 155, 504, 336],\n "label": "woman sitting on couch"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 2 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}, {'content': '<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.\n</think> Action: Path Tracer\n```json\n[{\n "index": 2,\n "start_point_2d": [335, 320],\n "end_point_2d": [232, 280],\n "label": "feet of man and boy"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 3 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}], '_response': [{'content': "<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.\n</think> Action: Answer\n<answer> A </answer>", 'role': 'assistant'}], '_system': 'system\n### Guidance:\nYou are a spatial reasoning assistant with access to two powerful visualization tools.\nYour task is to break down complex spatial problems and iteratively refine your solution through visualization feedback.\n\n### Available tools:\nYou can use the following two tools to visualize. After each tool usage, you must wait for and analyze the visualization feedback before proceeding.\n\n1. **Object Mapper**\n- Purpose: Identifies and maps key items in the space\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "bbox_2d": [x1, y1, x2, y2],\n "label": "object name/description"\n}]\n```\n- Output: Generates bounding boxes for visual inspection of the i-th image\n\n2. **Path Tracer**\n- Purpose: Plots movement or connections between points\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "start_point_2d": [x1, y1],\n "end_point_2d": [x2, y2],\n "label": "trace_description"\n}]\n```\n- Output: Generates visual paths for verification of the i-th image\n\n### Required Output Format:\nFor each reasoning step, you must structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: [Object Mapper/Path Tracer]\n```json\n[JSON format coordinates]\n```\n\nAfter your reasoning and iteratively refine your solution through visualization feedback, you should arrive at a final answer and structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: Answer\n<answer> [Your final answer] </answer>\n\n### Please NOTE the following reasoning techniques:\n1. Initial Analysis\n - Break down the spatial problem\n - Plan your approach\n\n2. Iterative Reasoning for Each Step\n - Choose appropriate tool\n - Provide absolute coordinates in JSON format (The top-left corner of the image is (0, 0) and the bottom-right corner is (504, 336))\n - Observe the visualization output\n - Reflect on the visualization:\n * Is the placement/path accurate?\n * Does it align with your reasoning?\n * What adjustments are needed?\n - Backtrack and Adjust:\n * If errors found, backtrack to previous step to modify actions or decisions as needed', '_tools': '', '_images': ['/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_0.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_1.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_2.png'], '_videos': None, '_audios': None}
Dataset({
features: ['_prompt', '_response', '_system', '_tools', '_images', '_videos', '_audios'],
num_rows: 33580
})
{'_prompt': [{'content': "<image>\n### Question:\nAre they touching feet?\nA) No\nB) Yes\nThe index of the given image is 1.\nAnswer with the option's letter from the given choices directly.\n\nBegin your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n", 'role': 'user'}, {'content': '<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.\n</think> Action: Object Mapper\n```json\n[{\n "index": 1,\n "bbox_2d": [124, 49, 389, 336],\n "label": "man holding remote"\n},\n{\n "index": 1,\n "bbox_2d": [201, 143, 286, 292],\n "label": "boy sitting in chair"\n},\n{\n "index": 1,\n "bbox_2d": [419, 155, 504, 336],\n "label": "woman sitting on couch"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 2 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}, {'content': '<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.\n</think> Action: Path Tracer\n```json\n[{\n "index": 2,\n "start_point_2d": [335, 320],\n "end_point_2d": [232, 280],\n "label": "feet of man and boy"\n}]\n```', 'role': 'assistant'}, {'content': '<image>\nThe index of the given image is 3 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:\n', 'role': 'user'}], '_response': [{'content': "<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.\n</think> Action: Answer\n<answer> A </answer>", 'role': 'assistant'}], '_system': 'system\n### Guidance:\nYou are a spatial reasoning assistant with access to two powerful visualization tools.\nYour task is to break down complex spatial problems and iteratively refine your solution through visualization feedback.\n\n### Available tools:\nYou can use the following two tools to visualize. After each tool usage, you must wait for and analyze the visualization feedback before proceeding.\n\n1. **Object Mapper**\n- Purpose: Identifies and maps key items in the space\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "bbox_2d": [x1, y1, x2, y2],\n "label": "object name/description"\n}]\n```\n- Output: Generates bounding boxes for visual inspection of the i-th image\n\n2. **Path Tracer**\n- Purpose: Plots movement or connections between points\n- Input format: JSON\n```json\n[{\n "index": i, # Image index\n "start_point_2d": [x1, y1],\n "end_point_2d": [x2, y2],\n "label": "trace_description"\n}]\n```\n- Output: Generates visual paths for verification of the i-th image\n\n### Required Output Format:\nFor each reasoning step, you must structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: [Object Mapper/Path Tracer]\n```json\n[JSON format coordinates]\n```\n\nAfter your reasoning and iteratively refine your solution through visualization feedback, you should arrive at a final answer and structure your response as follows:\n<think> [Your detailed reasoning process] </think> Action: Answer\n<answer> [Your final answer] </answer>\n\n### Please NOTE the following reasoning techniques:\n1. Initial Analysis\n - Break down the spatial problem\n - Plan your approach\n\n2. Iterative Reasoning for Each Step\n - Choose appropriate tool\n - Provide absolute coordinates in JSON format (The top-left corner of the image is (0, 0) and the bottom-right corner is (504, 336))\n - Observe the visualization output\n - Reflect on the visualization:\n * Is the placement/path accurate?\n * Does it align with your reasoning?\n * What adjustments are needed?\n - Backtrack and Adjust:\n * If errors found, backtrack to previous step to modify actions or decisions as needed', '_tools': '', '_images': ['/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_0.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_1.png', '/mnt/beegfs/dzhu6/ViLaSR-data/./cold_start/vqa/73685/images/image_2.png'], '_videos': None, '_audios': None}
Running tokenizer on dataset (num_proc=64): 2%|▏ | 525/33580 [00:55<57:44, 9.54 examples/s]
Running tokenizer on dataset (num_proc=64): 3%|▎ | 1050/33580 [00:55<23:28, 23.10 examples/s]
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Running tokenizer on dataset (num_proc=64): 11%|█ | 3675/33580 [00:56<03:01, 164.63 examples/s]
Running tokenizer on dataset (num_proc=64): 14%|█▍ | 4725/33580 [00:56<01:49, 263.68 examples/s]
Running tokenizer on dataset (num_proc=64): 17%|█▋ | 5775/33580 [00:56<01:08, 403.37 examples/s]
Running tokenizer on dataset (num_proc=64): 19%|█▉ | 6300/33580 [00:57<00:56, 486.86 examples/s]
Running tokenizer on dataset (num_proc=64): 22%|██▏ | 7350/33580 [00:57<00:35, 734.87 examples/s]
Running tokenizer on dataset (num_proc=64): 25%|██▌ | 8400/33580 [00:57<00:24, 1038.99 examples/s]
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Running tokenizer on dataset (num_proc=64): 42%|████▏ | 14175/33580 [03:15<04:38, 69.65 examples/s]
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training example:
input_ids:
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inputs:
<|im_start|>system
system
### Guidance:
You are a spatial reasoning assistant with access to two powerful visualization tools.
Your task is to break down complex spatial problems and iteratively refine your solution through visualization feedback.
### Available tools:
You can use the following two tools to visualize. After each tool usage, you must wait for and analyze the visualization feedback before proceeding.
1. **Object Mapper**
- Purpose: Identifies and maps key items in the space
- Input format: JSON
```json
[{
"index": i, # Image index
"bbox_2d": [x1, y1, x2, y2],
"label": "object name/description"
}]
```
- Output: Generates bounding boxes for visual inspection of the i-th image
2. **Path Tracer**
- Purpose: Plots movement or connections between points
- Input format: JSON
```json
[{
"index": i, # Image index
"start_point_2d": [x1, y1],
"end_point_2d": [x2, y2],
"label": "trace_description"
}]
```
- Output: Generates visual paths for verification of the i-th image
### Required Output Format:
For each reasoning step, you must structure your response as follows:
<think> [Your detailed reasoning process] </think> Action: [Object Mapper/Path Tracer]
```json
[JSON format coordinates]
```
After your reasoning and iteratively refine your solution through visualization feedback, you should arrive at a final answer and structure your response as follows:
<think> [Your detailed reasoning process] </think> Action: Answer
<answer> [Your final answer] </answer>
### Please NOTE the following reasoning techniques:
1. Initial Analysis
- Break down the spatial problem
- Plan your approach
2. Iterative Reasoning for Each Step
- Choose appropriate tool
- Provide absolute coordinates in JSON format (The top-left corner of the image is (0, 0) and the bottom-right corner is (504, 336))
- Observe the visualization output
- Reflect on the visualization:
* Is the placement/path accurate?
* Does it align with your reasoning?
* What adjustments are needed?
- Backtrack and Adjust:
* If errors found, backtrack to previous step to modify actions or decisions as needed<|im_end|>
<|im_start|>user
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### Question:
Are they touching feet?
A) No
B) Yes
The index of the given image is 1.
Answer with the option's letter from the given choices directly.
Begin your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:
<|im_end|>
<|im_start|>assistant
<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.
</think> Action: Object Mapper
```json
[{
"index": 1,
"bbox_2d": [124, 49, 389, 336],
"label": "man holding remote"
},
{
"index": 1,
"bbox_2d": [201, 143, 286, 292],
"label": "boy sitting in chair"
},
{
"index": 1,
"bbox_2d": [419, 155, 504, 336],
"label": "woman sitting on couch"
}]
```<|im_end|>
<|im_start|>user
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The index of the given image is 2 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:
<|im_end|>
<|im_start|>assistant
<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.
</think> Action: Path Tracer
```json
[{
"index": 2,
"start_point_2d": [335, 320],
"end_point_2d": [232, 280],
"label": "feet of man and boy"
}]
```<|im_end|>
<|im_start|>user
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The index of the given image is 3 (width: 504, height: 336). Continue your reasoning. After each tool use, critically evaluate the visualization and adjust if needed:
<|im_end|>
<|im_start|>assistant
<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.
</think> Action: Answer
<answer> A </answer><|im_end|>
label_ids:
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 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labels:
<think> To determine if they are touching feet, I need to first identify the feet of the people in the image. The Object Mapper can help with this by mapping out the people and their feet.
</think> Action: Object Mapper
```json
[{
"index": 1,
"bbox_2d": [124, 49, 389, 336],
"label": "man holding remote"
},
{
"index": 1,
"bbox_2d": [201, 143, 286, 292],
"label": "boy sitting in chair"
},
{
"index": 1,
"bbox_2d": [419, 155, 504, 336],
"label": "woman sitting on couch"
}]
```<|im_end|>
<think> The bounding boxes for the people have been identified. Now I need to focus on the feet of the man holding the remote and the boy sitting in the chair to see if they are touching. I will use the Path Tracer to draw lines from the feet of the man to the feet of the boy.
</think> Action: Path Tracer
```json
[{
"index": 2,
"start_point_2d": [335, 320],
"end_point_2d": [232, 280],
"label": "feet of man and boy"
}]
```<|im_end|>
<think> The visualization shows a line connecting the feet of the man and the boy, but it's clear from the image that they are not physically touching. The line is just a visual aid to check proximity. Since they are not touching, I can conclude the answer.
</think> Action: Answer
<answer> A </answer><|im_end|>
[INFO|configuration_utils.py:691] 2025-10-21 16:48:46,496 >> loading configuration file /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct/config.json
[INFO|configuration_utils.py:765] 2025-10-21 16:48:46,502 >> Model config Qwen2_5_VLConfig {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"image_token_id": 151655,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 128000,
"max_window_layers": 28,
"model_type": "qwen2_5_vl",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.1",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": 151656,
"vision_config": {
"depth": 32,
"fullatt_block_indexes": [
7,
15,
23,
31
],
"hidden_act": "silu",
"hidden_size": 1280,
"in_channels": 3,
"in_chans": 3,
"intermediate_size": 3420,
"model_type": "qwen2_5_vl",
"num_heads": 16,
"out_hidden_size": 3584,
"patch_size": 14,
"spatial_merge_size": 2,
"spatial_patch_size": 14,
"temporal_patch_size": 2,
"tokens_per_second": 2,
"window_size": 112
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 152064
}
[INFO|2025-10-21 16:48:46] llamafactory.model.model_utils.kv_cache:143 >> KV cache is disabled during training.
[INFO|modeling_utils.py:1121] 2025-10-21 16:48:46,562 >> loading weights file /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct/model.safetensors.index.json
[INFO|modeling_utils.py:3726] 2025-10-21 16:48:46,564 >> Detected DeepSpeed ZeRO-3: activating zero.init() for this model
[2025-10-21 16:48:46,565] [INFO] [config.py:735:__init__] Config mesh_device None world_size = 4
[INFO|configuration_utils.py:1142] 2025-10-21 16:48:46,573 >> Generate config GenerationConfig {
"bos_token_id": 151643,
"eos_token_id": 151645,
"use_cache": false
}
[INFO|modeling_utils.py:2167] 2025-10-21 16:48:46,573 >> Instantiating Qwen2_5_VisionTransformerPretrainedModel model under default dtype torch.float32.
[2025-10-21 16:48:46,794] [INFO] [config.py:735:__init__] Config mesh_device None world_size = 4
[2025-10-21 16:48:46,798] [INFO] [config.py:735:__init__] Config mesh_device None world_size = 4
[2025-10-21 16:48:46,891] [INFO] [config.py:735:__init__] Config mesh_device None world_size = 4
[2025-10-21 16:48:51,655] [INFO] [partition_parameters.py:348:__exit__] finished initializing model - num_params = 729, num_elems = 8.29B
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[INFO|modeling_utils.py:4930] 2025-10-21 16:49:28,258 >> All model checkpoint weights were used when initializing Qwen2_5_VLForConditionalGeneration.
[INFO|modeling_utils.py:4938] 2025-10-21 16:49:28,259 >> All the weights of Qwen2_5_VLForConditionalGeneration were initialized from the model checkpoint at /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct.
If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2_5_VLForConditionalGeneration for predictions without further training.
[INFO|configuration_utils.py:1095] 2025-10-21 16:49:28,264 >> loading configuration file /mnt/beegfs/dzhu6/Qwen2.5-VL-7B-Instruct/generation_config.json
[INFO|configuration_utils.py:1142] 2025-10-21 16:49:28,265 >> Generate config GenerationConfig {
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"pad_token_id": 151643,
"repetition_penalty": 1.05,
"temperature": 1e-06
}
[INFO|2025-10-21 16:49:28] llamafactory.model.model_utils.checkpointing:143 >> Gradient checkpointing enabled.
[INFO|2025-10-21 16:49:28] llamafactory.model.model_utils.attention:143 >> Using torch SDPA for faster training and inference.
[INFO|2025-10-21 16:49:28] llamafactory.model.adapter:143 >> DeepSpeed ZeRO3 detected, remaining trainable params in float32.
[INFO|2025-10-21 16:49:28] llamafactory.model.adapter:143 >> Fine-tuning method: Full
[INFO|2025-10-21 16:49:28] llamafactory.model.model_utils.visual:143 >> Set vision model not trainable: ['visual.patch_embed', 'visual.blocks'].
[INFO|2025-10-21 16:49:28] llamafactory.model.model_utils.visual:143 >> Set multi model projector not trainable: visual.merger.
[INFO|2025-10-21 16:49:28] llamafactory.model.loader:143 >> trainable params: 7,615,616,512 || all params: 8,292,166,656 || trainable%: 91.8411
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.
[INFO|trainer.py:748] 2025-10-21 16:49:28,437 >> Using auto half precision backend
[INFO|deepspeed.py:380] 2025-10-21 16:49:28,803 >> Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the custom optimizer has both CPU and GPU implementation (except LAMB)
Installed CUDA version 12.2 does not match the version torch was compiled with 12.6 but since the APIs are compatible, accepting this combination
Using /home/dzhu6/.cache/torch_extensions/py310_cu126 as PyTorch extensions root...
Detected CUDA files, patching ldflags
Emitting ninja build file /home/dzhu6/.cache/torch_extensions/py310_cu126/cpu_adam/build.ninja...
/mnt/beegfs/dzhu6/.miniconda3/envs/vilasr/lib/python3.10/site-packages/torch/utils/cpp_extension.py:2059: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
warnings.warn(
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
Installed CUDA version 12.2 does not match the version torch was compiled with 12.6 but since the APIs are compatible, accepting this combination
Using /home/dzhu6/.cache/torch_extensions/py310_cu126 as PyTorch extensions root...
Installed CUDA version 12.2 does not match the version torch was compiled with 12.6 but since the APIs are compatible, accepting this combination
Using /home/dzhu6/.cache/torch_extensions/py310_cu126 as PyTorch extensions root...
[1/1] c++ cpu_adam.o cpu_adam_impl.o -shared -lcurand -L/usr/local/cuda-12.2/lib64 -L/mnt/beegfs/dzhu6/.miniconda3/envs/vilasr/lib/python3.10/site-packages/torch/lib -lc10 -lc10_cuda -ltorch_cpu -ltorch_cuda -ltorch -ltorch_python -L/usr/local/cuda-12.2/lib64 -lcudart -o cpu_adam.so
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.0092740058898926 seconds
Installed CUDA version 12.2 does not match the version torch was compiled with 12.6 but since the APIs are compatible, accepting this combination
Using /home/dzhu6/.cache/torch_extensions/py310_cu126 as PyTorch extensions root...
Detected CUDA files, patching ldflags
Emitting ninja build file /home/dzhu6/.cache/torch_extensions/py310_cu126/cpu_adam/build.ninja...
/mnt/beegfs/dzhu6/.miniconda3/envs/vilasr/lib/python3.10/site-packages/torch/utils/cpp_extension.py:2059: UserWarning: TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'].
warnings.warn(
Building extension module cpu_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module cpu_adam...
Time to load cpu_adam op: 1.5777740478515625 seconds
Adam Optimizer #0 is created with AVX2 arithmetic capability.
Config: alpha=0.000010, betas=(0.900000, 0.999000), weight_decay=0.010000, adam_w=1
[2025-10-21 16:49:31,579] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed info: version=0.16.9, git-hash=unknown, git-branch=unknown
[2025-10-21 16:49:31,580] [INFO] [config.py:735:__init__] Config mesh_device None world_size = 4
[2025-10-21 16:49:31,593] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2025-10-21 16:49:31,594] [INFO] [logging.py:107:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2025-10-21 16:49:31,595] [INFO] [logging.py:107:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2025-10-21 16:49:31,613] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Basic Optimizer = DeepSpeedCPUAdam
[2025-10-21 16:49:31,614] [INFO] [utils.py:59:is_zero_supported_optimizer] Checking ZeRO support for optimizer=DeepSpeedCPUAdam type=<class 'deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam'>
[2025-10-21 16:49:31,614] [INFO] [logging.py:107:log_dist] [Rank 0] Creating fp16 ZeRO stage 3 optimizer, MiCS is enabled False, Hierarchical params gather False
[2025-10-21 16:49:31,615] [INFO] [logging.py:107:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 3 optimizer
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.1228344440460205 seconds
Loading extension module cpu_adam...
Time to load cpu_adam op: 2.15826153755188 seconds
[2025-10-21 16:49:31,852] [INFO] [utils.py:781:see_memory_usage] Stage 3 initialize beginning
[2025-10-21 16:49:31,854] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 3.05 GB CA 0.0 GB Max_CA 3 GB
[2025-10-21 16:49:31,854] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 61.03 GB, percent = 12.1%
[2025-10-21 16:49:31,857] [INFO] [stage3.py:170:__init__] Reduce bucket size 12845056
[2025-10-21 16:49:31,858] [INFO] [stage3.py:171:__init__] Prefetch bucket size 11560550
[2025-10-21 16:49:32,073] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [begin]
[2025-10-21 16:49:32,074] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:32,075] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 61.03 GB, percent = 12.1%
Parameter Offload: Total persistent parameters: 848896 in 368 params
[2025-10-21 16:49:32,397] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [end]
[2025-10-21 16:49:32,398] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:32,399] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 61.04 GB, percent = 12.1%
[2025-10-21 16:49:32,636] [INFO] [utils.py:781:see_memory_usage] Before creating fp16 partitions
[2025-10-21 16:49:32,637] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:32,638] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 61.04 GB, percent = 12.1%
[2025-10-21 16:49:35,956] [INFO] [utils.py:781:see_memory_usage] After creating fp16 partitions: 3
[2025-10-21 16:49:35,958] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:35,959] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 84.05 GB, percent = 16.7%
[2025-10-21 16:49:36,222] [INFO] [utils.py:781:see_memory_usage] Before creating fp32 partitions
[2025-10-21 16:49:36,223] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:36,224] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 91.27 GB, percent = 18.1%
[2025-10-21 16:49:37,716] [INFO] [utils.py:781:see_memory_usage] After creating fp32 partitions
[2025-10-21 16:49:37,717] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:37,718] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 105.72 GB, percent = 21.0%
[2025-10-21 16:49:37,976] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
[2025-10-21 16:49:37,977] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:37,978] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 114.2 GB, percent = 22.7%
[2025-10-21 16:49:44,396] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
[2025-10-21 16:49:44,397] [INFO] [utils.py:782:see_memory_usage] MA 0.0 GB Max_MA 0.0 GB CA 0.0 GB Max_CA 0 GB
[2025-10-21 16:49:44,398] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 138.23 GB, percent = 27.4%
[2025-10-21 16:49:44,399] [INFO] [stage3.py:534:_setup_for_real_optimizer] optimizer state initialized
[2025-10-21 16:49:48,591] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
[2025-10-21 16:49:48,593] [INFO] [utils.py:782:see_memory_usage] MA 0.02 GB Max_MA 2.05 GB CA 2.07 GB Max_CA 2 GB
[2025-10-21 16:49:48,593] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 156.34 GB, percent = 31.0%
[2025-10-21 16:49:48,594] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer_Stage3
[2025-10-21 16:49:48,594] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed using configured LR scheduler = None
[2025-10-21 16:49:48,595] [INFO] [logging.py:107:log_dist] [Rank 0] DeepSpeed LR Scheduler = None
[2025-10-21 16:49:48,595] [INFO] [logging.py:107:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0, 0.0], mom=[(0.9, 0.999), (0.9, 0.999)]
[2025-10-21 16:49:48,598] [INFO] [config.py:1003:print] DeepSpeedEngine configuration:
[2025-10-21 16:49:48,599] [INFO] [config.py:1007:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2025-10-21 16:49:48,599] [INFO] [config.py:1007:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'intra_op_parallelism': 1, 'single_submit': False, 'overlap_events': True, 'use_gds': False}
[2025-10-21 16:49:48,599] [INFO] [config.py:1007:print] amp_enabled .................. False
[2025-10-21 16:49:48,600] [INFO] [config.py:1007:print] amp_params ................... False
[2025-10-21 16:49:48,600] [INFO] [config.py:1007:print] autotuning_config ............ {
"enabled": false,
"start_step": null,
"end_step": null,
"metric_path": null,
"arg_mappings": null,
"metric": "throughput",
"model_info": null,
"results_dir": "autotuning_results",
"exps_dir": "autotuning_exps",
"overwrite": true,
"fast": true,
"start_profile_step": 3,
"end_profile_step": 5,
"tuner_type": "gridsearch",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"model_info_path": null,
"mp_size": 1,
"max_train_batch_size": null,
"min_train_batch_size": 1,
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
"min_train_micro_batch_size_per_gpu": 1,
"num_tuning_micro_batch_sizes": 3
}
[2025-10-21 16:49:48,601] [INFO] [config.py:1007:print] bfloat16_enabled ............. True
[2025-10-21 16:49:48,601] [INFO] [config.py:1007:print] bfloat16_immediate_grad_update True
[2025-10-21 16:49:48,602] [INFO] [config.py:1007:print] checkpoint_parallel_write_pipeline False
[2025-10-21 16:49:48,602] [INFO] [config.py:1007:print] checkpoint_tag_validation_enabled True
[2025-10-21 16:49:48,602] [INFO] [config.py:1007:print] checkpoint_tag_validation_fail False
[2025-10-21 16:49:48,602] [INFO] [config.py:1007:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x7fe1212bc520>
[2025-10-21 16:49:48,603] [INFO] [config.py:1007:print] communication_data_type ...... None
[2025-10-21 16:49:48,603] [INFO] [config.py:1007:print] compile_config ............... deepcompile=False free_activation=False offload_activation=False offload_opt_states=False double_buffer=True symmetric_memory=False debug_log=False offload_parameters=False sync_before_reduce=False sync_after_reduce=False sync_before_allgather=False sync_after_allgather=False
[2025-10-21 16:49:48,603] [INFO] [config.py:1007:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
[2025-10-21 16:49:48,603] [INFO] [config.py:1007:print] curriculum_enabled_legacy .... False
[2025-10-21 16:49:48,604] [INFO] [config.py:1007:print] curriculum_params_legacy ..... False
[2025-10-21 16:49:48,604] [INFO] [config.py:1007:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'pin_memory': False, 'curriculum_learning': {'enabled': False}, 'dynamic_batching': {'enabled': False, 'lr_scaling_method': 'linear', 'min_batch_size': 1, 'max_batch_size': None, 'sequence_picking_order': 'dataloader', 'verbose': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
[2025-10-21 16:49:48,604] [INFO] [config.py:1007:print] data_efficiency_enabled ...... False
[2025-10-21 16:49:48,605] [INFO] [config.py:1007:print] dataloader_drop_last ......... False
[2025-10-21 16:49:48,605] [INFO] [config.py:1007:print] disable_allgather ............ False
[2025-10-21 16:49:48,605] [INFO] [config.py:1007:print] dump_state ................... False
[2025-10-21 16:49:48,605] [INFO] [config.py:1007:print] dynamic_loss_scale_args ...... None
[2025-10-21 16:49:48,606] [INFO] [config.py:1007:print] eigenvalue_enabled ........... False
[2025-10-21 16:49:48,606] [INFO] [config.py:1007:print] eigenvalue_gas_boundary_resolution 1
[2025-10-21 16:49:48,606] [INFO] [config.py:1007:print] eigenvalue_layer_name ........ bert.encoder.layer
[2025-10-21 16:49:48,607] [INFO] [config.py:1007:print] eigenvalue_layer_num ......... 0
[2025-10-21 16:49:48,607] [INFO] [config.py:1007:print] eigenvalue_max_iter .......... 100
[2025-10-21 16:49:48,607] [INFO] [config.py:1007:print] eigenvalue_stability ......... 1e-06
[2025-10-21 16:49:48,607] [INFO] [config.py:1007:print] eigenvalue_tol ............... 0.01
[2025-10-21 16:49:48,608] [INFO] [config.py:1007:print] eigenvalue_verbose ........... False
[2025-10-21 16:49:48,608] [INFO] [config.py:1007:print] elasticity_enabled ........... False
[2025-10-21 16:49:48,608] [INFO] [config.py:1007:print] flops_profiler_config ........ {
"enabled": false,
"recompute_fwd_factor": 0.0,
"profile_step": 1,
"module_depth": -1,
"top_modules": 1,
"detailed": true,
"output_file": null
}
[2025-10-21 16:49:48,609] [INFO] [config.py:1007:print] fp16_auto_cast ............... None
[2025-10-21 16:49:48,609] [INFO] [config.py:1007:print] fp16_enabled ................. False
[2025-10-21 16:49:48,609] [INFO] [config.py:1007:print] fp16_master_weights_and_gradients False
[2025-10-21 16:49:48,610] [INFO] [config.py:1007:print] global_rank .................. 0
[2025-10-21 16:49:48,610] [INFO] [config.py:1007:print] grad_accum_dtype ............. None
[2025-10-21 16:49:48,610] [INFO] [config.py:1007:print] gradient_accumulation_steps .. 8
[2025-10-21 16:49:48,611] [INFO] [config.py:1007:print] gradient_clipping ............ 1.0
[2025-10-21 16:49:48,611] [INFO] [config.py:1007:print] gradient_predivide_factor .... 1.0
[2025-10-21 16:49:48,611] [INFO] [config.py:1007:print] graph_harvesting ............. False
[2025-10-21 16:49:48,611] [INFO] [config.py:1007:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
[2025-10-21 16:49:48,612] [INFO] [config.py:1007:print] initial_dynamic_scale ........ 1
[2025-10-21 16:49:48,612] [INFO] [config.py:1007:print] load_universal_checkpoint .... False
[2025-10-21 16:49:48,612] [INFO] [config.py:1007:print] loss_scale ................... 1.0
[2025-10-21 16:49:48,613] [INFO] [config.py:1007:print] memory_breakdown ............. False
[2025-10-21 16:49:48,613] [INFO] [config.py:1007:print] mics_hierarchial_params_gather False
[2025-10-21 16:49:48,613] [INFO] [config.py:1007:print] mics_shard_size .............. -1
[2025-10-21 16:49:48,614] [INFO] [config.py:1007:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName')
[2025-10-21 16:49:48,614] [INFO] [config.py:1007:print] nebula_config ................ {
"enabled": false,
"persistent_storage_path": null,
"persistent_time_interval": 100,
"num_of_version_in_retention": 2,
"enable_nebula_load": true,
"load_path": null
}
[2025-10-21 16:49:48,614] [INFO] [config.py:1007:print] optimizer_legacy_fusion ...... False
[2025-10-21 16:49:48,615] [INFO] [config.py:1007:print] optimizer_name ............... None
[2025-10-21 16:49:48,615] [INFO] [config.py:1007:print] optimizer_params ............. None
[2025-10-21 16:49:48,615] [INFO] [config.py:1007:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2025-10-21 16:49:48,615] [INFO] [config.py:1007:print] pld_enabled .................. False
[2025-10-21 16:49:48,616] [INFO] [config.py:1007:print] pld_params ................... False
[2025-10-21 16:49:48,616] [INFO] [config.py:1007:print] prescale_gradients ........... False
[2025-10-21 16:49:48,616] [INFO] [config.py:1007:print] scheduler_name ............... None
[2025-10-21 16:49:48,617] [INFO] [config.py:1007:print] scheduler_params ............. None
[2025-10-21 16:49:48,617] [INFO] [config.py:1007:print] seq_parallel_communication_data_type torch.float32
[2025-10-21 16:49:48,617] [INFO] [config.py:1007:print] sparse_attention ............. None
[2025-10-21 16:49:48,617] [INFO] [config.py:1007:print] sparse_gradients_enabled ..... False
[2025-10-21 16:49:48,618] [INFO] [config.py:1007:print] steps_per_print .............. inf
[2025-10-21 16:49:48,618] [INFO] [config.py:1007:print] tensor_parallel_config ....... dtype=torch.float16 autotp_size=0 tp_overlap_comm=False tensor_parallel=TPConfig(tp_size=1, tp_grain_size=1, mpu=None, tp_group=None) injection_policy_tuple=None keep_module_on_host=False replace_with_kernel_inject=False
[2025-10-21 16:49:48,618] [INFO] [config.py:1007:print] timers_config ................ enabled=True synchronized=True
[2025-10-21 16:49:48,619] [INFO] [config.py:1007:print] train_batch_size ............. 32
[2025-10-21 16:49:48,619] [INFO] [config.py:1007:print] train_micro_batch_size_per_gpu 1
[2025-10-21 16:49:48,619] [INFO] [config.py:1007:print] use_data_before_expert_parallel_ False
[2025-10-21 16:49:48,620] [INFO] [config.py:1007:print] use_node_local_storage ....... False
[2025-10-21 16:49:48,620] [INFO] [config.py:1007:print] wall_clock_breakdown ......... False
[2025-10-21 16:49:48,620] [INFO] [config.py:1007:print] weight_quantization_config ... None
[2025-10-21 16:49:48,621] [INFO] [config.py:1007:print] world_size ................... 4
[2025-10-21 16:49:48,621] [INFO] [config.py:1007:print] zero_allow_untested_optimizer True
[2025-10-21 16:49:48,621] [INFO] [config.py:1007:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=12845056 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500000000 overlap_comm=False load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='cpu', nvme_path=None, buffer_count=5, buffer_size=100000000, max_in_cpu=1000000000, pin_memory=True) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='cpu', nvme_path=None, buffer_count=4, pin_memory=True, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1000000000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=11560550 param_persistence_threshold=35840 model_persistence_threshold=9223372036854775807 max_live_parameters=1000000000 max_reuse_distance=1000000000 gather_16bit_weights_on_model_save=True module_granularity_threshold=0 use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False zeropp_loco_param=None mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=True pipeline_loading_checkpoint=False override_module_apply=True log_trace_cache_warnings=False
[2025-10-21 16:49:48,621] [INFO] [config.py:1007:print] zero_enabled ................. True
[2025-10-21 16:49:48,622] [INFO] [config.py:1007:print] zero_force_ds_cpu_optimizer .. True
[2025-10-21 16:49:48,622] [INFO] [config.py:1007:print] zero_optimization_stage ...... 3
[2025-10-21 16:49:48,622] [INFO] [config.py:993:print_user_config] json = {
"train_batch_size": 32,
"train_micro_batch_size_per_gpu": 1,
"gradient_accumulation_steps": 8,
"gradient_clipping": 1.0,
"zero_allow_untested_optimizer": true,
"fp16": {
"enabled": false,
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 3,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"offload_param": {
"device": "cpu",
"pin_memory": true
},
"overlap_comm": false,
"contiguous_gradients": true,
"sub_group_size": 1.000000e+09,
"reduce_bucket_size": 1.284506e+07,
"stage3_prefetch_bucket_size": 1.156055e+07,
"stage3_param_persistence_threshold": 3.584000e+04,
"stage3_max_live_parameters": 1.000000e+09,
"stage3_max_reuse_distance": 1.000000e+09,
"stage3_gather_16bit_weights_on_model_save": true
},
"steps_per_print": inf
}
[INFO|trainer.py:2414] 2025-10-21 16:49:48,628 >> ***** Running training *****
[INFO|trainer.py:2415] 2025-10-21 16:49:48,629 >> Num examples = 33,580
[INFO|trainer.py:2416] 2025-10-21 16:49:48,629 >> Num Epochs = 3
[INFO|trainer.py:2417] 2025-10-21 16:49:48,629 >> Instantaneous batch size per device = 1
[INFO|trainer.py:2420] 2025-10-21 16:49:48,630 >> Total train batch size (w. parallel, distributed & accumulation) = 32
[INFO|trainer.py:2421] 2025-10-21 16:49:48,630 >> Gradient Accumulation steps = 8
[INFO|trainer.py:2422] 2025-10-21 16:49:48,630 >> Total optimization steps = 3,147
[INFO|trainer.py:2423] 2025-10-21 16:49:48,633 >> Number of trainable parameters = 7,615,616,512
0%| | 0/3147 [00:00<?, ?it/s]
0%| | 1/3147 [02:45<144:18:30, 165.13s/it]
0%| | 2/3147 [05:30<144:26:17, 165.33s/it]
0%| | 3/3147 [08:29<149:53:25, 171.63s/it]
0%| | 4/3147 [11:16<148:17:01, 169.84s/it]
0%| | 5/3147 [14:17<151:36:16, 173.70s/it]
0%| | 6/3147 [17:23<155:06:38, 177.78s/it]
0%| | 7/3147 [20:41<160:51:54, 184.43s/it]
0%| | 8/3147 [23:47<161:20:57, 185.05s/it]
0%| | 9/3147 [27:03<164:15:00, 188.43s/it]slurmstepd-c31: error: *** JOB 154774 ON c31 CANCELLED AT 2025-10-21T17:19:43 ***
|