[trainer] feat: Optimize tokenizer decode calls in validation

[none0663]

What does this PR do?

Optimize the _validate method by conditionally executing tokenizer decode operations only when needed.

Changes:

• Added conditional check for val_data_dir and generations_to_log to determine if decoding is required
• Made input_texts and output_texts decoding conditional
• Removed duplicate val_data_dir definition

Performance Impact:
When val_data_dir is not set and log_val_generations is 0, tokenizer decode operations are skipped entirely, reducing unnecessary computational overhead during validation.

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[gemini-code-assist]

Code Review

This pull request introduces a valuable optimization by making tokenizer decode operations conditional within the _validate method. The logic to skip decoding when val_data_dir is not set and log_val_generations is zero is sound and should reduce computational overhead during validation. The removal of the duplicate val_data_dir definition is also a good cleanup.

I've added a couple of suggestions to further improve performance and code consistency by using tokenizer.batch_decode instead of iterating with tokenizer.decode. This is more efficient and aligns with practices in other parts of the file.

[gemini-code-assist]

For better performance and consistency with other parts of the codebase (e.g., _log_rollout_data), it's recommended to use tokenizer.batch_decode instead of a list comprehension with tokenizer.decode. batch_decode is generally more efficient for decoding batches of sequences. You can also directly extend sample_inputs to make the code more concise.

Suggested change

	input_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in input_ids]
	sample_inputs.extend(input_texts)
	sample_inputs.extend(self.tokenizer.batch_decode(input_ids, skip_special_tokens=True))

[gemini-code-assist]

Similar to the input decoding, using tokenizer.batch_decode here is more performant and consistent with other parts of the code. The intermediate output_texts variable can also be removed for conciseness.

Suggested change

	output_texts = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in output_ids]
	sample_outputs.extend(output_texts)
	sample_outputs.extend(self.tokenizer.batch_decode(output_ids, skip_special_tokens=True))