Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- added_tokens.json +24 -0
- args.json +366 -0
- chat_template.json +3 -0
- config.json +66 -0
- generation_config.json +12 -0
- latest +1 -0
- merges.txt +0 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +736 -0
- preprocessor_config.json +19 -0
- scheduler.pt +3 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +209 -0
- trainer_state.json +483 -0
- training_args.bin +3 -0
- vocab.json +0 -0
- zero_to_fp32.py +760 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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added_tokens.json
ADDED
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@@ -0,0 +1,24 @@
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{
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}
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args.json
ADDED
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@@ -0,0 +1,366 @@
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| 1 |
+
{
|
| 2 |
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"model": "/mllm_hdd/yfzhang/data/models--Qwen--Qwen2.5-VL-7B-Instruct/snapshots/cc594898137f460bfe9f0759e9844b3ce807cfb5",
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"model_type": "qwen2_5_vl",
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"task_type": "causal_lm",
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"attn_impl": "flash_attn",
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"template": "qwen2_5_vl",
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"system": null,
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"max_length": 20480,
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"padding_free": false,
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"padding_side": "right",
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"loss_scale": "default",
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"sequence_parallel_size": 1,
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"use_chat_template": true,
|
| 28 |
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"template_backend": "swift",
|
| 29 |
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"dataset": [
|
| 30 |
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"/mllm_hdd/yfzhang/Agent-R1/construct_data/math_and_chart/arxivqa/arxivqa_processed_excuted_2_quality_training_v2_28k_v5.jsonl"
|
| 31 |
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],
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"val_dataset": [],
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"split_dataset_ratio": 0.01,
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"data_seed": 42,
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"dataset_num_proc": 1,
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"load_from_cache_file": true,
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"dataset_shuffle": true,
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"val_dataset_shuffle": false,
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"streaming": false,
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"interleave_prob": null,
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"stopping_strategy": "first_exhausted",
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"shuffle_buffer_size": 1000,
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"download_mode": "reuse_dataset_if_exists",
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"columns": {},
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"strict": false,
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"remove_unused_columns": true,
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"model_name": [
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],
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],
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"hqq_axis": null,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"bnb_4bit_quant_storage": null,
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"max_new_tokens": 64,
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"temperature": 0.0,
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"num_beams": 1,
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"stream": false,
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"stop_words": [],
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"logprobs": false,
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"top_logprobs": null,
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"lora_modules": [],
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"tuner_backend": "peft",
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"train_type": "full",
|
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"adapters": [],
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"external_plugins": [],
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"seed": 42,
|
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"model_kwargs": {},
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"load_args": false,
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"load_data_args": false,
|
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"use_hf": false,
|
| 84 |
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"hub_token": null,
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"custom_register_path": [],
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"ddp_timeout": 1800,
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"ddp_backend": null,
|
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"ignore_args_error": false,
|
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|
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"output_dir": "/mmu_mllm_hdd/yfzhang/Agent-R1/agent_latest_code/models/qwen_tool_training_file_swift_28k_arxivqa_28k_v5/v0-20250527-210915",
|
| 91 |
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"overwrite_output_dir": false,
|
| 92 |
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"do_train": false,
|
| 93 |
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"do_eval": false,
|
| 94 |
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"do_predict": false,
|
| 95 |
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"eval_strategy": "epoch",
|
| 96 |
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|
| 97 |
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"per_device_train_batch_size": 1,
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"learning_rate": 1e-05,
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"num_train_epochs": 3.0,
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| 112 |
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"max_steps": -1,
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|
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"log_level": "passive",
|
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"log_level_replica": "warning",
|
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"log_on_each_node": true,
|
| 120 |
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"logging_dir": "/mmu_mllm_hdd/yfzhang/Agent-R1/agent_latest_code/models/qwen_tool_training_file_swift_28k_arxivqa_28k_v5/v0-20250527-210915/runs",
|
| 121 |
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"logging_strategy": "steps",
|
| 122 |
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"logging_first_step": true,
|
| 123 |
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"logging_steps": 5,
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| 124 |
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"logging_nan_inf_filter": true,
|
| 125 |
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"save_strategy": "epoch",
|
| 126 |
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"save_steps": 500,
|
| 127 |
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"save_total_limit": 5,
|
| 128 |
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"save_safetensors": true,
|
| 129 |
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"save_on_each_node": false,
|
| 130 |
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"save_only_model": false,
|
| 131 |
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"restore_callback_states_from_checkpoint": false,
|
| 132 |
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"no_cuda": false,
|
| 133 |
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"use_cpu": false,
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| 134 |
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"use_mps_device": false,
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"jit_mode_eval": false,
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| 136 |
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"use_ipex": false,
|
| 137 |
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"bf16": true,
|
| 138 |
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"fp16": false,
|
| 139 |
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"fp16_opt_level": "O1",
|
| 140 |
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"half_precision_backend": "auto",
|
| 141 |
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"bf16_full_eval": false,
|
| 142 |
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"fp16_full_eval": false,
|
| 143 |
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"tf32": null,
|
| 144 |
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"local_rank": 0,
|
| 145 |
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"tpu_num_cores": null,
|
| 146 |
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"tpu_metrics_debug": false,
|
| 147 |
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"debug": null,
|
| 148 |
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"dataloader_drop_last": false,
|
| 149 |
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"eval_steps": null,
|
| 150 |
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|
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|
| 300 |
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|
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|
| 317 |
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|
| 318 |
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| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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| 323 |
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|
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|
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|
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| 330 |
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|
| 331 |
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|
| 332 |
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|
| 333 |
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|
| 334 |
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|
| 335 |
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|
| 336 |
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|
| 337 |
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|
| 338 |
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|
| 339 |
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|
| 340 |
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|
| 341 |
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|
| 342 |
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|
| 343 |
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|
| 344 |
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|
| 345 |
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|
| 346 |
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|
| 347 |
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|
| 348 |
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|
| 349 |
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|
| 350 |
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|
| 351 |
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|
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|
| 354 |
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|
| 355 |
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| 356 |
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|
| 357 |
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|
| 358 |
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"local_world_size": 8,
|
| 359 |
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"model_suffix": "Qwen2.5-VL-7B-Instruct",
|
| 360 |
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"model_info": "ModelInfo(model_type='qwen2_5_vl', model_dir='/mllm_hdd/yfzhang/data/models--Qwen--Qwen2.5-VL-7B-Instruct/snapshots/cc594898137f460bfe9f0759e9844b3ce807cfb5', torch_dtype=torch.bfloat16, max_model_len=128000, quant_method=None, quant_bits=None, rope_scaling={'type': 'default', 'mrope_section': [16, 24, 24], 'rope_type': 'default'}, config=None, task_type='causal_lm', num_labels=None)",
|
| 361 |
+
"model_meta": "ModelMeta(model_type='qwen2_5_vl', model_groups=[ModelGroup(models=[Model(ms_model_id='Qwen/Qwen2.5-VL-3B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-3B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-7B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-7B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-32B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-32B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-72B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-72B-Instruct', model_path=None, ms_revision=None, hf_revision=None)], ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/Qwen2.5-VL-3B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-3B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-7B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-7B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-32B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-32B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-72B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-72B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None)], ignore_patterns=None, requires=None, tags=[])], template='qwen2_5_vl', get_function=<function get_model_tokenizer_qwen2_5_vl at 0x7ff617740d30>, model_arch='qwen2_vl', architectures=['Qwen2_5_VLForConditionalGeneration'], additional_saved_files=[], torch_dtype=None, is_multimodal=True, is_reward=False, task_type=None, ignore_patterns=None, requires=['transformers>=4.49', 'qwen_vl_utils>=0.0.6', 'decord'], tags=[])",
|
| 362 |
+
"model_dir": "/mllm_hdd/yfzhang/data/models--Qwen--Qwen2.5-VL-7B-Instruct/snapshots/cc594898137f460bfe9f0759e9844b3ce807cfb5",
|
| 363 |
+
"hub": "<class 'swift.hub.hub.MSHub'>",
|
| 364 |
+
"evaluation_strategy": "epoch",
|
| 365 |
+
"training_args": "Seq2SeqTrainingArguments(output_dir='/mmu_mllm_hdd/yfzhang/Agent-R1/agent_latest_code/models/qwen_tool_training_file_swift_28k_arxivqa_28k_v5/v0-20250527-210915', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, eval_strategy=<IntervalStrategy.EPOCH: 'epoch'>, prediction_loss_only=False, per_device_train_batch_size=1, per_device_eval_batch_size=1, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=16, eval_accumulation_steps=None, eval_delay=0, torch_empty_cache_steps=None, learning_rate=1e-05, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=3.0, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs=None, warmup_ratio=0.05, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='/mmu_mllm_hdd/yfzhang/Agent-R1/agent_latest_code/models/qwen_tool_training_file_swift_28k_arxivqa_28k_v5/v0-20250527-210915/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<SaveStrategy.EPOCH: 'epoch'>, save_steps=500, save_total_limit=5, save_safetensors=True, save_on_each_node=False, save_only_model=False, restore_callback_states_from_checkpoint=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=42, jit_mode_eval=False, use_ipex=False, bf16=True, fp16=False, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend=None, tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=None, dataloader_num_workers=2, dataloader_prefetch_factor=10, past_index=-1, run_name='/mmu_mllm_hdd/yfzhang/Agent-R1/agent_latest_code/models/qwen_tool_training_file_swift_28k_arxivqa_28k_v5/v0-20250527-210915', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, tp_size=0, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), deepspeed={'fp16': {'enabled': 'auto', 'loss_scale': 0, 'loss_scale_window': 1000, 'initial_scale_power': 16, 'hysteresis': 2, 'min_loss_scale': 1}, 'bf16': {'enabled': 'auto'}, 'zero_optimization': {'stage': 2, 'offload_optimizer': {'device': 'none', 'pin_memory': True}, 'allgather_partitions': True, 'allgather_bucket_size': 200000000.0, 'overlap_comm': False, 'reduce_scatter': True, 'reduce_bucket_size': 200000000.0, 'contiguous_gradients': True}, 'gradient_accumulation_steps': 'auto', 'gradient_clipping': 'auto', 'steps_per_print': 2000, 'train_batch_size': 'auto', 'train_micro_batch_size_per_gpu': 'auto', 'wall_clock_breakdown': False}, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['tensorboard'], ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=None, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, include_for_metrics=[], eval_do_concat_batches=True, fp16_backend='auto', push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, include_tokens_per_second=None, include_num_input_tokens_seen=None, neftune_noise_alpha=None, optim_target_modules=None, batch_eval_metrics=False, eval_on_start=False, use_liger_kernel=False, eval_use_gather_object=False, average_tokens_across_devices=None, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, check_model=True, acc_strategy='token', train_dataloader_shuffle=True, max_epochs=None, aligner_lr=None, vit_lr=None, optimizer=None, metric_warmup_step=0, fsdp_num=1, acc_steps=1, eval_use_evalscope=False, eval_datasets=[], eval_limit=None, eval_datasets_args=None, eval_generation_config=None, train_type='full', local_repo_path=None, galore_config=None)"
|
| 366 |
+
}
|
chat_template.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
| 3 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,66 @@
|
|
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generation_config.json
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preprocessor_config.json
ADDED
|
@@ -0,0 +1,19 @@
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|
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|
| 1 |
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{
|
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"min_pixels": 3136,
|
| 3 |
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"max_pixels": 12845056,
|
| 4 |
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"patch_size": 14,
|
| 5 |
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"temporal_patch_size": 2,
|
| 6 |
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"merge_size": 2,
|
| 7 |
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|
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|
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|
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|
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|
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|
| 17 |
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"image_processor_type": "Qwen2VLImageProcessor",
|
| 18 |
+
"processor_class": "Qwen2_5_VLProcessor"
|
| 19 |
+
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|
scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:4d2fc698a1ff72742355e2e525a9be54a1a6eff82cf301510dfc37ddd5986052
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| 3 |
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size 1064
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
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|
| 1 |
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{
|
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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],
|
| 17 |
+
"eos_token": {
|
| 18 |
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"content": "<|im_end|>",
|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
+
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|
| 31 |
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|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
| 3 |
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size 11421896
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,209 @@
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|
| 1 |
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{
|
| 2 |
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"add_bos_token": false,
|
| 3 |
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"add_prefix_space": false,
|
| 4 |
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"added_tokens_decoder": {
|
| 5 |
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|
| 6 |
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|
| 7 |
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|
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|
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|
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|
| 12 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
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|
| 19 |
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|
| 20 |
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},
|
| 21 |
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"151645": {
|
| 22 |
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"content": "<|im_end|>",
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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},
|
| 29 |
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"151646": {
|
| 30 |
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"content": "<|object_ref_start|>",
|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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},
|
| 37 |
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"151647": {
|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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| 45 |
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| 46 |
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|
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|
| 49 |
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|
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|
| 52 |
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|
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|
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| 59 |
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|
| 60 |
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|
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|
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|
| 70 |
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| 72 |
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|
| 76 |
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|
| 78 |
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| 79 |
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| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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| 93 |
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| 94 |
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|
| 100 |
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| 101 |
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|
| 102 |
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|
| 103 |
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|
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|
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|
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|
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| 109 |
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|
| 110 |
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|
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
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|
| 129 |
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|
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|
| 131 |
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|
| 132 |
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|
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|
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|
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|
| 139 |
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|
| 140 |
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| 141 |
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|
| 142 |
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|
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|
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|
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| 149 |
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|
| 150 |
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|
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|
| 158 |
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| 159 |
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"lstrip": false,
|
| 160 |
+
"normalized": false,
|
| 161 |
+
"rstrip": false,
|
| 162 |
+
"single_word": false,
|
| 163 |
+
"special": false
|
| 164 |
+
},
|
| 165 |
+
"151663": {
|
| 166 |
+
"content": "<|repo_name|>",
|
| 167 |
+
"lstrip": false,
|
| 168 |
+
"normalized": false,
|
| 169 |
+
"rstrip": false,
|
| 170 |
+
"single_word": false,
|
| 171 |
+
"special": false
|
| 172 |
+
},
|
| 173 |
+
"151664": {
|
| 174 |
+
"content": "<|file_sep|>",
|
| 175 |
+
"lstrip": false,
|
| 176 |
+
"normalized": false,
|
| 177 |
+
"rstrip": false,
|
| 178 |
+
"single_word": false,
|
| 179 |
+
"special": false
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
"additional_special_tokens": [
|
| 183 |
+
"<|im_start|>",
|
| 184 |
+
"<|im_end|>",
|
| 185 |
+
"<|object_ref_start|>",
|
| 186 |
+
"<|object_ref_end|>",
|
| 187 |
+
"<|box_start|>",
|
| 188 |
+
"<|box_end|>",
|
| 189 |
+
"<|quad_start|>",
|
| 190 |
+
"<|quad_end|>",
|
| 191 |
+
"<|vision_start|>",
|
| 192 |
+
"<|vision_end|>",
|
| 193 |
+
"<|vision_pad|>",
|
| 194 |
+
"<|image_pad|>",
|
| 195 |
+
"<|video_pad|>"
|
| 196 |
+
],
|
| 197 |
+
"bos_token": null,
|
| 198 |
+
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
| 199 |
+
"clean_up_tokenization_spaces": false,
|
| 200 |
+
"eos_token": "<|im_end|>",
|
| 201 |
+
"errors": "replace",
|
| 202 |
+
"extra_special_tokens": {},
|
| 203 |
+
"model_max_length": 131072,
|
| 204 |
+
"pad_token": "<|endoftext|>",
|
| 205 |
+
"processor_class": "Qwen2_5_VLProcessor",
|
| 206 |
+
"split_special_tokens": false,
|
| 207 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 208 |
+
"unk_token": null
|
| 209 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,483 @@
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|
| 1 |
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{
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| 2 |
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"memory(GiB)": 47.64,
|
| 448 |
+
"step": 215,
|
| 449 |
+
"token_acc": 0.8889558426532973,
|
| 450 |
+
"train_speed(iter/s)": 0.129582
|
| 451 |
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},
|
| 452 |
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{
|
| 453 |
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"epoch": 1.0,
|
| 454 |
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"eval_loss": 0.34231579303741455,
|
| 455 |
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"eval_runtime": 5.2453,
|
| 456 |
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"eval_samples_per_second": 53.0,
|
| 457 |
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"eval_steps_per_second": 6.673,
|
| 458 |
+
"eval_token_acc": 0.8946564779533734,
|
| 459 |
+
"step": 216
|
| 460 |
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}
|
| 461 |
+
],
|
| 462 |
+
"logging_steps": 5,
|
| 463 |
+
"max_steps": 645,
|
| 464 |
+
"num_input_tokens_seen": 0,
|
| 465 |
+
"num_train_epochs": 3,
|
| 466 |
+
"save_steps": 500,
|
| 467 |
+
"stateful_callbacks": {
|
| 468 |
+
"TrainerControl": {
|
| 469 |
+
"args": {
|
| 470 |
+
"should_epoch_stop": false,
|
| 471 |
+
"should_evaluate": false,
|
| 472 |
+
"should_log": false,
|
| 473 |
+
"should_save": true,
|
| 474 |
+
"should_training_stop": false
|
| 475 |
+
},
|
| 476 |
+
"attributes": {}
|
| 477 |
+
}
|
| 478 |
+
},
|
| 479 |
+
"total_flos": 2.7232866958552596e+18,
|
| 480 |
+
"train_batch_size": 1,
|
| 481 |
+
"trial_name": null,
|
| 482 |
+
"trial_params": null
|
| 483 |
+
}
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5f616725581c133063988539ddbd7485dafe6ac0648f4466bf224032d16e53aa
|
| 3 |
+
size 7992
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
zero_to_fp32.py
ADDED
|
@@ -0,0 +1,760 @@
|
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|
| 1 |
+
#!/usr/bin/env python
|
| 2 |
+
|
| 3 |
+
# Copyright (c) Microsoft Corporation.
|
| 4 |
+
# SPDX-License-Identifier: Apache-2.0
|
| 5 |
+
|
| 6 |
+
# DeepSpeed Team
|
| 7 |
+
|
| 8 |
+
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
| 9 |
+
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
| 10 |
+
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
| 11 |
+
# application.
|
| 12 |
+
#
|
| 13 |
+
# example:
|
| 14 |
+
# python zero_to_fp32.py . output_dir/
|
| 15 |
+
# or
|
| 16 |
+
# python zero_to_fp32.py . output_dir/ --safe_serialization
|
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import argparse
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import torch
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import glob
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import math
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import os
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import re
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import gc
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import json
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import numpy as np
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from tqdm import tqdm
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from collections import OrderedDict
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from dataclasses import dataclass
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# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
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# DeepSpeed data structures it has to be available in the current python environment.
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from deepspeed.utils import logger
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from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
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FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
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FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
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@dataclass
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class zero_model_state:
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buffers: dict()
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param_shapes: dict()
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shared_params: list
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ds_version: int
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frozen_param_shapes: dict()
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frozen_param_fragments: dict()
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debug = 0
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# load to cpu
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device = torch.device('cpu')
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def atoi(text):
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return int(text) if text.isdigit() else text
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def natural_keys(text):
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'''
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alist.sort(key=natural_keys) sorts in human order
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http://nedbatchelder.com/blog/200712/human_sorting.html
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(See Toothy's implementation in the comments)
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'''
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return [atoi(c) for c in re.split(r'(\d+)', text)]
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def get_model_state_file(checkpoint_dir, zero_stage):
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if not os.path.isdir(checkpoint_dir):
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raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
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# there should be only one file
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if zero_stage <= 2:
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file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
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elif zero_stage == 3:
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file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
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if not os.path.exists(file):
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raise FileNotFoundError(f"can't find model states file at '{file}'")
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return file
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def get_checkpoint_files(checkpoint_dir, glob_pattern):
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# XXX: need to test that this simple glob rule works for multi-node setup too
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ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
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if len(ckpt_files) == 0:
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raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
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return ckpt_files
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def get_optim_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
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def get_model_state_files(checkpoint_dir):
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return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
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def parse_model_states(files):
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zero_model_states = []
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for file in files:
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state_dict = torch.load(file, map_location=device, weights_only=False)
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if BUFFER_NAMES not in state_dict:
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raise ValueError(f"{file} is not a model state checkpoint")
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buffer_names = state_dict[BUFFER_NAMES]
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if debug:
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print("Found buffers:", buffer_names)
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# recover just the buffers while restoring them to fp32 if they were saved in fp16
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buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
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param_shapes = state_dict[PARAM_SHAPES]
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# collect parameters that are included in param_shapes
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param_names = []
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for s in param_shapes:
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for name in s.keys():
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param_names.append(name)
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# update with frozen parameters
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frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
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if frozen_param_shapes is not None:
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if debug:
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print(f"Found frozen_param_shapes: {frozen_param_shapes}")
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param_names += list(frozen_param_shapes.keys())
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# handle shared params
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shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
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ds_version = state_dict.get(DS_VERSION, None)
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frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
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z_model_state = zero_model_state(buffers=buffers,
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param_shapes=param_shapes,
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shared_params=shared_params,
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ds_version=ds_version,
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frozen_param_shapes=frozen_param_shapes,
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frozen_param_fragments=frozen_param_fragments)
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zero_model_states.append(z_model_state)
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return zero_model_states
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def parse_optim_states(files, ds_checkpoint_dir):
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total_files = len(files)
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state_dicts = []
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for f in tqdm(files, desc='Loading checkpoint shards'):
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state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
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# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
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# and also handle the case where it was already removed by another helper script
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state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
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state_dicts.append(state_dict)
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if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
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raise ValueError(f"{files[0]} is not a zero checkpoint")
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zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
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world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
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# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
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# parameters can be different from data parallelism for non-expert parameters. So we can just
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# use the max of the partition_count to get the dp world_size.
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if type(world_size) is list:
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world_size = max(world_size)
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if world_size != total_files:
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raise ValueError(
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f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
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"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
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)
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# the groups are named differently in each stage
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if zero_stage <= 2:
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fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
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elif zero_stage == 3:
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fp32_groups_key = FP32_FLAT_GROUPS
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else:
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raise ValueError(f"unknown zero stage {zero_stage}")
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fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
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return zero_stage, world_size, fp32_flat_groups
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def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
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"""
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Returns fp32 state_dict reconstructed from ds checkpoint
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Args:
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- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
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"""
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print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
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optim_files = get_optim_files(ds_checkpoint_dir)
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zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
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print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
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model_files = get_model_state_files(ds_checkpoint_dir)
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zero_model_states = parse_model_states(model_files)
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print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
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if zero_stage <= 2:
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return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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elif zero_stage == 3:
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return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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exclude_frozen_parameters)
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def _zero2_merge_frozen_params(state_dict, zero_model_states):
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if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
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return
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frozen_param_shapes = zero_model_states[0].frozen_param_shapes
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frozen_param_fragments = zero_model_states[0].frozen_param_fragments
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if debug:
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num_elem = sum(s.numel() for s in frozen_param_shapes.values())
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print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
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wanted_params = len(frozen_param_shapes)
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wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
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avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
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print(f'Frozen params: Have {avail_numel} numels to process.')
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print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
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total_params = 0
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total_numel = 0
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for name, shape in frozen_param_shapes.items():
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total_params += 1
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unpartitioned_numel = shape.numel()
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total_numel += unpartitioned_numel
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state_dict[name] = frozen_param_fragments[name]
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
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def _has_callable(obj, fn):
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attr = getattr(obj, fn, None)
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return callable(attr)
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def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
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param_shapes = zero_model_states[0].param_shapes
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# Reconstruction protocol:
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#
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# XXX: document this
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if debug:
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for i in range(world_size):
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for j in range(len(fp32_flat_groups[0])):
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print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
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# XXX: memory usage doubles here (zero2)
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num_param_groups = len(fp32_flat_groups[0])
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merged_single_partition_of_fp32_groups = []
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for i in range(num_param_groups):
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merged_partitions = [sd[i] for sd in fp32_flat_groups]
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full_single_fp32_vector = torch.cat(merged_partitions, 0)
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merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
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avail_numel = sum(
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[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
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if debug:
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wanted_params = sum([len(shapes) for shapes in param_shapes])
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wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
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# not asserting if there is a mismatch due to possible padding
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print(f"Have {avail_numel} numels to process.")
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print(f"Need {wanted_numel} numels in {wanted_params} params.")
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# params
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# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
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# out-of-core computing solution
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total_numel = 0
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total_params = 0
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for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
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offset = 0
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avail_numel = full_single_fp32_vector.numel()
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for name, shape in shapes.items():
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unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
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total_numel += unpartitioned_numel
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total_params += 1
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+
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if debug:
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print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
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state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
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offset += unpartitioned_numel
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+
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# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
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# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
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# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
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# live optimizer object, so we are checking that the numbers are within the right range
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align_to = 2 * world_size
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def zero2_align(x):
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return align_to * math.ceil(x / align_to)
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+
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if debug:
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print(f"original offset={offset}, avail_numel={avail_numel}")
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+
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offset = zero2_align(offset)
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avail_numel = zero2_align(avail_numel)
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+
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if debug:
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print(f"aligned offset={offset}, avail_numel={avail_numel}")
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+
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# Sanity check
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if offset != avail_numel:
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raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
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+
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print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
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+
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| 324 |
+
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+
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
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| 326 |
+
exclude_frozen_parameters):
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+
state_dict = OrderedDict()
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| 328 |
+
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+
# buffers
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| 330 |
+
buffers = zero_model_states[0].buffers
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| 331 |
+
state_dict.update(buffers)
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| 332 |
+
if debug:
|
| 333 |
+
print(f"added {len(buffers)} buffers")
|
| 334 |
+
|
| 335 |
+
if not exclude_frozen_parameters:
|
| 336 |
+
_zero2_merge_frozen_params(state_dict, zero_model_states)
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| 337 |
+
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| 338 |
+
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
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| 339 |
+
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+
# recover shared parameters
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| 341 |
+
for pair in zero_model_states[0].shared_params:
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| 342 |
+
if pair[1] in state_dict:
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+
state_dict[pair[0]] = state_dict[pair[1]]
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+
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+
return state_dict
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+
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+
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+
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
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| 349 |
+
remainder = unpartitioned_numel % world_size
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| 350 |
+
padding_numel = (world_size - remainder) if remainder else 0
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| 351 |
+
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
| 352 |
+
return partitioned_numel, padding_numel
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+
|
| 354 |
+
|
| 355 |
+
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
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| 356 |
+
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
| 357 |
+
return
|
| 358 |
+
|
| 359 |
+
if debug:
|
| 360 |
+
for i in range(world_size):
|
| 361 |
+
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
| 362 |
+
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
| 363 |
+
|
| 364 |
+
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
| 365 |
+
wanted_params = len(frozen_param_shapes)
|
| 366 |
+
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
| 367 |
+
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
| 368 |
+
print(f'Frozen params: Have {avail_numel} numels to process.')
|
| 369 |
+
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
| 370 |
+
|
| 371 |
+
total_params = 0
|
| 372 |
+
total_numel = 0
|
| 373 |
+
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
| 374 |
+
total_params += 1
|
| 375 |
+
unpartitioned_numel = shape.numel()
|
| 376 |
+
total_numel += unpartitioned_numel
|
| 377 |
+
|
| 378 |
+
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
| 379 |
+
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
| 380 |
+
|
| 381 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 382 |
+
|
| 383 |
+
if debug:
|
| 384 |
+
print(
|
| 385 |
+
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 386 |
+
)
|
| 387 |
+
|
| 388 |
+
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
| 389 |
+
|
| 390 |
+
|
| 391 |
+
class GatheredTensor:
|
| 392 |
+
"""
|
| 393 |
+
A pseudo tensor that collects partitioned weights.
|
| 394 |
+
It is more memory efficient when there are multiple groups.
|
| 395 |
+
"""
|
| 396 |
+
|
| 397 |
+
def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
|
| 398 |
+
self.flat_groups = flat_groups
|
| 399 |
+
self.flat_groups_offset = flat_groups_offset
|
| 400 |
+
self.offset = offset
|
| 401 |
+
self.partitioned_numel = partitioned_numel
|
| 402 |
+
self.shape = shape
|
| 403 |
+
self.dtype = self.flat_groups[0][0].dtype
|
| 404 |
+
|
| 405 |
+
def contiguous(self):
|
| 406 |
+
"""
|
| 407 |
+
Merge partitioned weights from flat_groups into a single tensor.
|
| 408 |
+
"""
|
| 409 |
+
end_idx = self.offset + self.partitioned_numel
|
| 410 |
+
world_size = len(self.flat_groups)
|
| 411 |
+
pad_flat_param_chunks = []
|
| 412 |
+
|
| 413 |
+
for rank_i in range(world_size):
|
| 414 |
+
# for each rank, we need to collect weights from related group/groups
|
| 415 |
+
flat_groups_at_rank_i = self.flat_groups[rank_i]
|
| 416 |
+
start_group_id = None
|
| 417 |
+
end_group_id = None
|
| 418 |
+
for group_id in range(len(self.flat_groups_offset)):
|
| 419 |
+
if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
|
| 420 |
+
start_group_id = group_id
|
| 421 |
+
if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
|
| 422 |
+
end_group_id = group_id
|
| 423 |
+
break
|
| 424 |
+
# collect weights from related group/groups
|
| 425 |
+
for group_id in range(start_group_id, end_group_id + 1):
|
| 426 |
+
flat_tensor = flat_groups_at_rank_i[group_id]
|
| 427 |
+
start_offset = self.offset - self.flat_groups_offset[group_id]
|
| 428 |
+
end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
|
| 429 |
+
pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
|
| 430 |
+
|
| 431 |
+
# collect weights from all ranks
|
| 432 |
+
pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
|
| 433 |
+
param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
|
| 434 |
+
return param
|
| 435 |
+
|
| 436 |
+
|
| 437 |
+
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
| 438 |
+
param_shapes = zero_model_states[0].param_shapes
|
| 439 |
+
avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
|
| 440 |
+
|
| 441 |
+
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
| 442 |
+
# param, re-consolidating each param, while dealing with padding if any
|
| 443 |
+
|
| 444 |
+
# merge list of dicts, preserving order
|
| 445 |
+
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
| 446 |
+
|
| 447 |
+
if debug:
|
| 448 |
+
for i in range(world_size):
|
| 449 |
+
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
| 450 |
+
|
| 451 |
+
wanted_params = len(param_shapes)
|
| 452 |
+
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
| 453 |
+
# not asserting if there is a mismatch due to possible padding
|
| 454 |
+
avail_numel = fp32_flat_groups[0].numel() * world_size
|
| 455 |
+
print(f"Trainable params: Have {avail_numel} numels to process.")
|
| 456 |
+
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
| 457 |
+
|
| 458 |
+
# params
|
| 459 |
+
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
| 460 |
+
# out-of-core computing solution
|
| 461 |
+
offset = 0
|
| 462 |
+
total_numel = 0
|
| 463 |
+
total_params = 0
|
| 464 |
+
flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
|
| 465 |
+
for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
|
| 466 |
+
unpartitioned_numel = shape.numel()
|
| 467 |
+
total_numel += unpartitioned_numel
|
| 468 |
+
total_params += 1
|
| 469 |
+
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
| 470 |
+
|
| 471 |
+
if debug:
|
| 472 |
+
print(
|
| 473 |
+
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
| 474 |
+
)
|
| 475 |
+
|
| 476 |
+
# memory efficient tensor
|
| 477 |
+
tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
|
| 478 |
+
state_dict[name] = tensor
|
| 479 |
+
offset += partitioned_numel
|
| 480 |
+
|
| 481 |
+
offset *= world_size
|
| 482 |
+
|
| 483 |
+
# Sanity check
|
| 484 |
+
if offset != avail_numel:
|
| 485 |
+
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
| 486 |
+
|
| 487 |
+
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
| 488 |
+
|
| 489 |
+
|
| 490 |
+
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
| 491 |
+
exclude_frozen_parameters):
|
| 492 |
+
state_dict = OrderedDict()
|
| 493 |
+
|
| 494 |
+
# buffers
|
| 495 |
+
buffers = zero_model_states[0].buffers
|
| 496 |
+
state_dict.update(buffers)
|
| 497 |
+
if debug:
|
| 498 |
+
print(f"added {len(buffers)} buffers")
|
| 499 |
+
|
| 500 |
+
if not exclude_frozen_parameters:
|
| 501 |
+
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
| 502 |
+
|
| 503 |
+
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
| 504 |
+
|
| 505 |
+
# recover shared parameters
|
| 506 |
+
for pair in zero_model_states[0].shared_params:
|
| 507 |
+
if pair[1] in state_dict:
|
| 508 |
+
state_dict[pair[0]] = state_dict[pair[1]]
|
| 509 |
+
|
| 510 |
+
return state_dict
|
| 511 |
+
|
| 512 |
+
|
| 513 |
+
def to_torch_tensor(state_dict, return_empty_tensor=False):
|
| 514 |
+
"""
|
| 515 |
+
Convert state_dict of GatheredTensor to torch tensor
|
| 516 |
+
"""
|
| 517 |
+
torch_state_dict = {}
|
| 518 |
+
converted_tensors = {}
|
| 519 |
+
for name, tensor in state_dict.items():
|
| 520 |
+
tensor_id = id(tensor)
|
| 521 |
+
if tensor_id in converted_tensors: # shared tensors
|
| 522 |
+
shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
|
| 523 |
+
torch_state_dict[name] = shared_tensor
|
| 524 |
+
else:
|
| 525 |
+
converted_tensors[tensor_id] = name
|
| 526 |
+
if return_empty_tensor:
|
| 527 |
+
torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
|
| 528 |
+
else:
|
| 529 |
+
torch_state_dict[name] = tensor.contiguous()
|
| 530 |
+
return torch_state_dict
|
| 531 |
+
|
| 532 |
+
|
| 533 |
+
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 534 |
+
tag=None,
|
| 535 |
+
exclude_frozen_parameters=False,
|
| 536 |
+
lazy_mode=False):
|
| 537 |
+
"""
|
| 538 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
| 539 |
+
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
| 540 |
+
via a model hub.
|
| 541 |
+
|
| 542 |
+
Args:
|
| 543 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder
|
| 544 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
| 545 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 546 |
+
- ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
|
| 547 |
+
Convert the pesduo tensor to torch tensor by ``.contiguous()``
|
| 548 |
+
|
| 549 |
+
Returns:
|
| 550 |
+
- pytorch ``state_dict``
|
| 551 |
+
|
| 552 |
+
A typical usage might be ::
|
| 553 |
+
|
| 554 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 555 |
+
# do the training and checkpoint saving
|
| 556 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
| 557 |
+
model = model.cpu() # move to cpu
|
| 558 |
+
model.load_state_dict(state_dict)
|
| 559 |
+
# submit to model hub or save the model to share with others
|
| 560 |
+
|
| 561 |
+
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
| 562 |
+
application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 563 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 564 |
+
|
| 565 |
+
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
| 566 |
+
|
| 567 |
+
Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
|
| 568 |
+
You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
| 569 |
+
the checkpoint. Or you can load state_dict in lazy mode ::
|
| 570 |
+
|
| 571 |
+
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
| 572 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
|
| 573 |
+
for name, lazy_tensor in state_dict.item():
|
| 574 |
+
tensor = lazy_tensor.contiguous() # to cpu
|
| 575 |
+
print(name, tensor)
|
| 576 |
+
# del tensor to release memory if it no longer in use
|
| 577 |
+
"""
|
| 578 |
+
if tag is None:
|
| 579 |
+
latest_path = os.path.join(checkpoint_dir, 'latest')
|
| 580 |
+
if os.path.isfile(latest_path):
|
| 581 |
+
with open(latest_path, 'r') as fd:
|
| 582 |
+
tag = fd.read().strip()
|
| 583 |
+
else:
|
| 584 |
+
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
| 585 |
+
|
| 586 |
+
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
| 587 |
+
|
| 588 |
+
if not os.path.isdir(ds_checkpoint_dir):
|
| 589 |
+
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
| 590 |
+
|
| 591 |
+
state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
| 592 |
+
if lazy_mode:
|
| 593 |
+
return state_dict
|
| 594 |
+
else:
|
| 595 |
+
return to_torch_tensor(state_dict)
|
| 596 |
+
|
| 597 |
+
|
| 598 |
+
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
|
| 599 |
+
output_dir,
|
| 600 |
+
max_shard_size="5GB",
|
| 601 |
+
safe_serialization=False,
|
| 602 |
+
tag=None,
|
| 603 |
+
exclude_frozen_parameters=False):
|
| 604 |
+
"""
|
| 605 |
+
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
| 606 |
+
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
| 607 |
+
|
| 608 |
+
Args:
|
| 609 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 610 |
+
- ``output_dir``: directory to the pytorch fp32 state_dict output files
|
| 611 |
+
- ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
|
| 612 |
+
- ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
|
| 613 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 614 |
+
- ``exclude_frozen_parameters``: exclude frozen parameters
|
| 615 |
+
"""
|
| 616 |
+
|
| 617 |
+
# Dependency pre-check
|
| 618 |
+
if safe_serialization:
|
| 619 |
+
try:
|
| 620 |
+
from safetensors.torch import save_file
|
| 621 |
+
except ImportError:
|
| 622 |
+
print('If you want to use `safe_serialization`, please `pip install safetensors`')
|
| 623 |
+
raise
|
| 624 |
+
if max_shard_size is not None:
|
| 625 |
+
try:
|
| 626 |
+
from huggingface_hub import split_torch_state_dict_into_shards
|
| 627 |
+
except ImportError:
|
| 628 |
+
print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
|
| 629 |
+
raise
|
| 630 |
+
|
| 631 |
+
# Convert zero checkpoint to state_dict
|
| 632 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
|
| 633 |
+
tag,
|
| 634 |
+
exclude_frozen_parameters,
|
| 635 |
+
lazy_mode=True)
|
| 636 |
+
|
| 637 |
+
# Shard the model if it is too big.
|
| 638 |
+
weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
|
| 639 |
+
if max_shard_size is not None:
|
| 640 |
+
filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
|
| 641 |
+
# an memory-efficient approach for sharding
|
| 642 |
+
empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
|
| 643 |
+
state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
|
| 644 |
+
filename_pattern=filename_pattern,
|
| 645 |
+
max_shard_size=max_shard_size)
|
| 646 |
+
else:
|
| 647 |
+
from collections import namedtuple
|
| 648 |
+
StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
|
| 649 |
+
state_dict_split = StateDictSplit(is_sharded=False,
|
| 650 |
+
filename_to_tensors={weights_name: list(state_dict.keys())})
|
| 651 |
+
|
| 652 |
+
# Save the model by shard
|
| 653 |
+
os.makedirs(output_dir, exist_ok=True)
|
| 654 |
+
filename_to_tensors = state_dict_split.filename_to_tensors.items()
|
| 655 |
+
for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
|
| 656 |
+
shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
|
| 657 |
+
shard_state_dict = to_torch_tensor(shard_state_dict)
|
| 658 |
+
output_path = os.path.join(output_dir, shard_file)
|
| 659 |
+
if safe_serialization:
|
| 660 |
+
save_file(shard_state_dict, output_path, metadata={"format": "pt"})
|
| 661 |
+
else:
|
| 662 |
+
torch.save(shard_state_dict, output_path)
|
| 663 |
+
# release the memory of current shard
|
| 664 |
+
for tensor_name in list(shard_state_dict.keys()):
|
| 665 |
+
del state_dict[tensor_name]
|
| 666 |
+
del shard_state_dict[tensor_name]
|
| 667 |
+
del shard_state_dict
|
| 668 |
+
gc.collect()
|
| 669 |
+
|
| 670 |
+
# Save index if sharded
|
| 671 |
+
if state_dict_split.is_sharded:
|
| 672 |
+
index = {
|
| 673 |
+
"metadata": state_dict_split.metadata,
|
| 674 |
+
"weight_map": state_dict_split.tensor_to_filename,
|
| 675 |
+
}
|
| 676 |
+
save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
|
| 677 |
+
save_index_file = os.path.join(output_dir, save_index_file)
|
| 678 |
+
with open(save_index_file, "w", encoding="utf-8") as f:
|
| 679 |
+
content = json.dumps(index, indent=2, sort_keys=True) + "\n"
|
| 680 |
+
f.write(content)
|
| 681 |
+
|
| 682 |
+
|
| 683 |
+
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
| 684 |
+
"""
|
| 685 |
+
1. Put the provided model to cpu
|
| 686 |
+
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
| 687 |
+
3. Load it into the provided model
|
| 688 |
+
|
| 689 |
+
Args:
|
| 690 |
+
- ``model``: the model object to update
|
| 691 |
+
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
| 692 |
+
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
| 693 |
+
|
| 694 |
+
Returns:
|
| 695 |
+
- ``model`: modified model
|
| 696 |
+
|
| 697 |
+
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
| 698 |
+
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
| 699 |
+
conveniently placed for you in the checkpoint folder.
|
| 700 |
+
|
| 701 |
+
A typical usage might be ::
|
| 702 |
+
|
| 703 |
+
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
| 704 |
+
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
| 705 |
+
# submit to model hub or save the model to share with others
|
| 706 |
+
|
| 707 |
+
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
| 708 |
+
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
| 709 |
+
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
| 710 |
+
|
| 711 |
+
"""
|
| 712 |
+
logger.info(f"Extracting fp32 weights")
|
| 713 |
+
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
| 714 |
+
|
| 715 |
+
logger.info(f"Overwriting model with fp32 weights")
|
| 716 |
+
model = model.cpu()
|
| 717 |
+
model.load_state_dict(state_dict, strict=False)
|
| 718 |
+
|
| 719 |
+
return model
|
| 720 |
+
|
| 721 |
+
|
| 722 |
+
if __name__ == "__main__":
|
| 723 |
+
parser = argparse.ArgumentParser()
|
| 724 |
+
parser.add_argument("checkpoint_dir",
|
| 725 |
+
type=str,
|
| 726 |
+
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
| 727 |
+
parser.add_argument("output_dir",
|
| 728 |
+
type=str,
|
| 729 |
+
help="directory to the pytorch fp32 state_dict output files"
|
| 730 |
+
"(e.g. path/checkpoint-12-output/)")
|
| 731 |
+
parser.add_argument(
|
| 732 |
+
"--max_shard_size",
|
| 733 |
+
type=str,
|
| 734 |
+
default="5GB",
|
| 735 |
+
help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
|
| 736 |
+
"lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
|
| 737 |
+
"We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
|
| 738 |
+
"without CPU OOM issues.")
|
| 739 |
+
parser.add_argument(
|
| 740 |
+
"--safe_serialization",
|
| 741 |
+
default=False,
|
| 742 |
+
action='store_true',
|
| 743 |
+
help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
|
| 744 |
+
parser.add_argument("-t",
|
| 745 |
+
"--tag",
|
| 746 |
+
type=str,
|
| 747 |
+
default=None,
|
| 748 |
+
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
| 749 |
+
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
| 750 |
+
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
| 751 |
+
args = parser.parse_args()
|
| 752 |
+
|
| 753 |
+
debug = args.debug
|
| 754 |
+
|
| 755 |
+
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
| 756 |
+
args.output_dir,
|
| 757 |
+
max_shard_size=args.max_shard_size,
|
| 758 |
+
safe_serialization=args.safe_serialization,
|
| 759 |
+
tag=args.tag,
|
| 760 |
+
exclude_frozen_parameters=args.exclude_frozen_parameters)
|