SageMaker训练Mistral-7B时进度条显示步数与计算值不符的咨询

我按照以下两篇博客的流程，在Amazon SageMaker上微调mistral-7B-instruct-v01模型：

• 《How to Fine-Tune LLMs in 2024 with Hugging Face》
• 《Train and Deploy Mistral 7B with Hugging Face on Amazon SageMaker》

微调后的模型效果良好，但我对训练过程中的进度条存在疑问。

当使用包含100条样本的小数据集，设置如下参数进行微调时：

• num_train_epochs = 2
• per_device_batch_size = 1
• gradient_accumulation_steps = 4
• SM_LOG_LEVEL=20
• logging_steps = 25

得到的训练进度条如下：

0%|          | 0/4 [00:00<?, ?it/s]
25%|██▌       | 1/4 [00:24<01:14, 24.76s/it]
50%|█████     | 2/4 [00:49<00:49, 24.51s/it]
75%|███████▌  | 3/4 [01:13<00:24, 24.47s/it]
100%|██████████| 4/4 [01:37<00:00, 24.42s/it]
{'train_runtime': 97.8848, 'train_samples_per_second': 0.184, 'train_steps_per_second': 0.041, 'train_loss': 1.038140892982483, 'epoch': 1.78}
100%|██████████| 4/4 [01:37<00:00, 24.42s/it]
100%|██████████| 4/4 [01:37<00:00, 24.47s/it]

当使用包含10000条样本的数据集时，进度条最终迭代情况如下：

100%|█████████▉| 491/492 [3:19:46<00:24, 24.41s/it]
100%|██████████| 492/492 [3:20:10<00:00, 24.40s/it]
{'train_runtime': 12010.6264, 'train_samples_per_second': 0.164, 'train_steps_per_second': 0.041, 'train_loss': 0.5181044475819038, 'epoch': 2.0}
100%|██████████| 492/492 [3:20:10<00:00, 24.40s/it]
100%|██████████| 492/492 [3:20:10<00:00, 24.41s/it]

我无法理解进度条的迭代更新逻辑：
对于100条样本的数据集，根据参数计算，总训练步数应为(100×2)/4=50步；对于10000条样本的数据集，总步数应为(10000×2)/4=5000步。但进度条却分别显示4步和492步，请问该差异的原因是什么？

job_name = f'mistralinstruct-7b-hf-mini'

hyperparameters = {
  'dataset_path': '/opt/ml/input/data/training/train_dataset.json',
  'model_id': "mistralai/Mistral-7B-Instruct-v0.1",
  'max_seq_len': 3872,
  'use_qlora': True,
  'num_train_epochs': 2,
  'per_device_train_batch_size': 1,
  'gradient_accumulation_steps': 4,
  'gradient_checkpointing': True,
  'optim': "adamw_torch_fused",
  'logging_steps': 25,
  'save_strategy': "steps",
  'save_steps' : 100,
  'learning_rate': 2e-4,
  'bf16': True,
  'tf32': True, 
  'max_grad_norm': 1.0,
  'warmup_ratio': 0.03,
  'lr_scheduler_type': "constant",
  'report_to': "tensorboard",
  'output_dir': "/opt/ml/checkpoints",
  'merge_adapters': True,
}


sess = sagemaker.Session()
# sagemaker session bucket -> used for uploading data, models and logs
# sagemaker will automatically create this bucket if it not exists
sagemaker_session_bucket='com.ravenpack.dsteam.research.testing'
if sagemaker_session_bucket is None and sess is not None:
    # set to default bucket if a bucket name is not given
    sagemaker_session_bucket = sess.default_bucket()
print(sagemaker_session_bucket)

try:
    role = sagemaker.get_execution_role()
except ValueError:
    iam = boto3.client('iam')
    role = iam.get_role(RoleName='SageMaker-ds-research-testing')['Role']['Arn']

sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)


tensorboard_output_config = TensorBoardOutputConfig(
    container_local_output_path='/opt/ml/output/tensorboard',
    s3_output_path = f's3://{sess.default_bucket()}/...{my_path}...',
)


metric_definitions = [
    {'Name': 'loss', 'Regex': "'loss':\s*([0-9\\.]+)"},
    {'Name': 'grad_norm', 'Regex': "'grad_norm':\s*([0-9\\.]+)"},
    {'Name': 'learning_rate', 'Regex': "'learning_rate':\s*([0-9\\.]+)"},
    {'Name': 'epoch', 'Regex': "'epoch':\s*([0-9\\.]+)"}
]


# create the Estimator
huggingface_estimator = HuggingFace(
    entry_point          = 'run_sft.py',    # train script (used Philip's from https://github.com/philschmid/llm-sagemaker-sample/blob/main/scripts/trl/run_sft.py)
    source_dir           = '...{my_path}...', 
    instance_type        = 'ml.g5.4xlarge',
    instance_count       = 1,             
    max_run              = 1*24*60*60,
    max_wait             = 2*24*60*60,       
    use_spot_instances   = True,
    base_job_name        = job_name,         
    role                 = role,
    volume_size          = 300,
    transformers_version = '4.36',
    pytorch_version      = '2.1',
    py_version           = 'py310',
    hyperparameters      =  hyperparameters,
    disable_output_compression = True,
    environment          = {
                            "HUGGINGFACE_HUB_CACHE": "/tmp/.cache",
                            },
    metric_definitions   = metric_definitions,
    tensorboard_output_config = tensorboard_output_config,
    
    checkpoint_s3_uri = f's3://{sess.default_bucket()}/...{my_path}...',
)   


training_input_path = f's3://{sess.default_bucket()}/...{my_path}...'


data = {'training': training_input_path}

# starting the train job with our uploaded datasets as input
huggingface_estimator.fit(data, wait=True)

内容的提问来源于stack exchange，提问作者Axel Sjöberg