The easiest method is to set these DeepSpeed config values to 'auto'. [rank1]: Traceback (most recent call last): [rank1]: File "/data1/users/heyu/find_size_and_weight/train711.py", line 625, in <module> [rank1]: train() [rank1]: File "/data1/users/heyu/find_size_and_weight/train711.py", line 617, in train [rank1]: train_result = trainer.train() [rank1]: File "/data1/users/heyu/uv_env/pyhy/lib/python3.10/site-packages/transformers/trainer.py", line 2240, in train [rank1]: return inner_training_loop( [rank1]: File "/data1/users/heyu/uv_env/pyhy/lib/python3.10/site-packages/transformers/trainer.py", line 2322, in _inner_training_loop [rank1]: self.optimizer, self.lr_scheduler = deepspeed_init(self, num_training_steps=max_steps) [rank1]: File "/data1/users/heyu/uv_env/pyhy/lib/python3.10/site-packages/transformers/integrations/deepspeed.py", line 444, in deepspeed_init [rank1]: hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps) [rank1]: File "/data1/users/heyu/uv_env/pyhy/lib/python3.10/site-packages/transformers/integrations/deepspeed.py", line 268, in trainer_config_finalize [rank1]: raise ValueError( [rank1]: ValueError: Please correct the following DeepSpeed config values that mismatch TrainingArguments values: [rank1]: - ds scheduler.params.warmup_max_lr=0.0001 vs hf learning_rate=1e-05 [rank1]: The easiest method is to set these DeepSpeed config values to 'auto'. 2025-07-11 16:02:32,818 - ERROR - Training failed: Please correct the following DeepSpeed config values that mismatch TrainingArguments values: - ds scheduler.params.warmup_max_lr=0.0001 vs hf learning_rate=1e-05 The easiest method is to set these DeepSpeed config values to 'auto'.上述代码报错，修改

<think>我们遇到了DeepSpeed配置错误，具体是DeepSpeed的scheduler配置中的warmup_max_lr与TrainingArguments中的learning_rate不匹配。 根据错误信息，DeepSpeed配置中scheduler.params.warmup_max_lr被设置为0.0001（即1e-4），而TrainingArguments中的learning_rate为1e-5。 错误提示建议将这些值设置为'auto'，这样DeepSpeed会自动使用TrainingArguments中的值。 修改方法：将DeepSpeed配置中scheduler.params.warmup_max_lr设置为'auto'，同时检查其他相关参数是否也需要设置为'auto'。 在代码中，我们找到DeepSpeed配置部分，将scheduler.params.warmup_max_lr改为'auto'，同时将warmup_min_lr也改为'auto'（因为错误提示中只提到了warmup_max_lr，但为了安全，将整个scheduler部分设置为自动）。 另外，注意错误信息还提到了其他可能不匹配的值，但当前只报告了warmup_max_lr。因此，我们按照错误提示，将scheduler部分的相关参数都设置为'auto'。 修改后的DeepSpeed配置部分如下： 原始配置： "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": 0, "warmup_max_lr": 1e-4, "warmup_num_steps": "auto", "total_num_steps": "auto", } }, 修改为： "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto", } }, 这样，DeepSpeed就会自动从TrainingArguments中获取学习率相关的设置。 同时，我们注意到在TrainingArguments中已经设置了learning_rate=1e-5，所以DeepSpeed会使用这个值。 修改代码中的CONFIG字典的deepspeed_config部分。 注意：由于我们使用的是DeepSpeed的配置文件（这里是以字典形式给出），需要确保所有参数设置正确。 另外，错误信息中还提到“The easiest method is to set these DeepSpeed config values to 'auto'.”，所以我们按照这个建议修改。 修改后重新运行。</think>错误原因是 DeepSpeed 配置中的学习率参数与 TrainingArguments 中的学习率设置不一致。让我们修复这个问题： ```python def train(): TRAIN_FILES = ["./data/train_26.4k_20250703.jsonl"] VAL_FILES = ['./data/test_1.0k_20250703.jsonl'] CONFIG = { # ... 其他配置保持不变 ... "deepspeed_config": { "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "optimizer": { "type": "AdamW", "params": { "lr": "auto", # 确保这里设置为 auto "weight_decay": "auto", "betas": "auto", } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", # 修改为 auto "warmup_max_lr": "auto", # 修改为 auto "warmup_num_steps": "auto", "total_num_steps": "auto", } }, "fp16": { "enabled": "auto", }, "bf16": { "enabled": "auto", }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "none", "pin_memory": True }, "allgather_partitions": True, "allgather_bucket_size": 2e8, "overlap_comm": True, "reduce_scatter": True, "reduce_bucket_size": 2e8, "contiguous_gradients": True }, "gradient_clipping": 1.0, "steps_per_print": 5, "wall_clock_breakdown": False } } # ... 后续代码保持不变 ... ``` ### 关键修改点： 1. **DeepSpeed 调度器配置**： ```python "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", # 修改为 auto "warmup_max_lr": "auto", # 修改为 auto "warmup_num_steps": "auto", "total_num_steps": "auto", } } ``` 2. **优化器配置**： ```python "optimizer": { "type": "AdamW", "params": { "lr": "auto", # 确保这里设置为 auto # ... } } ``` ### 完整修复后的代码： ```python def train(): TRAIN_FILES = ["./data/train_26.4k_20250703.jsonl"] VAL_FILES = ['./data/test_1.0k_20250703.jsonl'] CONFIG = { "model_path": '/nas_data/xiao/models/Qwen2.5-VL-7B-Instruct', "processor_path": '/nas_data/xiao/models/Qwen2.5-VL-7B-Instruct', "train_files": TRAIN_FILES, "val_files": VAL_FILES, "max_pixels": 301056, "max_length": 4096, "output_dir": "./output", "learning_rate": 1e-5, "batch_size": 4, "grad_accum_steps": 2, "num_epochs": 1, "warmup_ratio": 0.05, "weight_decay": 0.1, "logging_steps": 5, "eval_steps": 500, "save_steps": 500, "save_total_limit": 5, "fp16": False, "bf16": True, "gradient_checkpointing": True, "deepspeed_config": { "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "optimizer": { "type": "AdamW", "params": { "lr": "auto", "weight_decay": "auto", "betas": "auto", } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto", } }, "fp16": { "enabled": "auto", }, "bf16": { "enabled": "auto", }, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "none", "pin_memory": True }, "allgather_partitions": True, "allgather_bucket_size": 2e8, "overlap_comm": True, "reduce_scatter": True, "reduce_bucket_size": 2e8, "contiguous_gradients": True }, "gradient_clipping": 1.0, "steps_per_print": 5, "wall_clock_breakdown": False } } # ... 后续代码保持不变 ... ``` ### 为什么这样修改？ 1. **DeepSpeed 的 'auto' 模式**： - 当设置为 'auto' 时，DeepSpeed 会自动从 TrainingArguments 中获取相应的值 - 这确保了 DeepSpeed 配置与 Hugging Face Trainer 配置的一致性 2. **错误原因分析**： ```bash - ds scheduler.params.warmup_max_lr=0.0001 vs hf learning_rate=1e-05 ``` - 原始代码中固定设置了 `warmup_max_lr=1e-4`，但 TrainingArguments 使用的是 `1e-5` - 使用 'auto' 让 DeepSpeed 自动匹配 TrainingArguments 中的学习率设置 3. **最佳实践**： - 在 DeepSpeed 配置中，所有与 Trainer 相关的参数都应设置为 'auto' - 这样可以避免手动同步配置，减少配置错误 这个修改将解决 DeepSpeed 配置与 TrainingArguments 之间的不一致问题，确保训练能够正常启动。