python - Memory bottleneck when tokenizing large datasets with Hugging Face’s AutoTokenizer - Stack Overflow

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I’m encountering a memory bottleneck when tokenizing large datasets with Hugging Face’s AutoTokenizer for LLM pretraining. Despite using batch_encode_plus with truncation, the process intermittently crashes. Are there strategies or tools to dynamically manage tokenization memory during batch processing?

Here’s a response tailored to Question 2 that adheres to Stack Overflow’s guidelines:

What I tried:
I first attempted to use Hugging Face's Trainer class for fine-tuning the model. I set the eval_steps parameter to a lower value (e.g., 50) to trigger evaluations more frequently and provided both compute_metrics and evaluation_strategy in the TrainingArguments. My initial code looked something like this:

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="steps",
    eval_steps=50,
    logging_dir="./logs",
    compute_metrics=my_compute_metrics,
    save_steps=100,
)

However, this resulted in unexpected behavior where the evaluation was either skipped entirely or not producing any metrics.

I also tried using DataCollator to ensure the dataset's formatting was consistent with the model's tokenizer output, but it didn't resolve the problem. Here's an example snippet:

from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer,
    data_collator=data_collator,
)

Additionally, I manually checked the evaluation dataset using trainer.evaluate() before training to ensure it was being correctly processed. It returned valid outputs when invoked directly, which makes the absence of metrics during training perplexing.

What I expected:
I expected the Trainer to periodically evaluate the model on the evaluation dataset during training, compute the specified metrics (e.g., accuracy, F1 score), and log them alongside other training statistics.

For instance, the evaluation logs should display something like this:

{'eval_loss': 0.2456, 'eval_accuracy': 0.92, 'step': 100}

Instead, no evaluation logs are being generated, and the training process completes without any intermediate evaluation outputs.

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