fix: remove unnecessary movement of eval logits to cpu

[NanoCode012]

Description

Edit: See #2824 (comment)

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There has been reports about eval taking more vram than training. I suspect it's this config which we set to grad accu. Would love some tests for this!

This config does not seem to be running mini-batches as we think?

https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments.eval_accumulation_steps

Motivation and Context

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Summary by CodeRabbit

• Refactor
  • Removed explicit setting of the "eval_accumulation_steps" parameter in training configuration.

[coderabbitai]

Walkthrough

The update removes the explicit assignment of the "eval_accumulation_steps" parameter in the training arguments within the causal builder module. This parameter, previously set to mirror "gradient_accumulation_steps", is no longer included during the construction of training arguments.

Changes

File(s)	Change Summary
src/axolotl/core/builders/causal.py	Removed assignment of "eval_accumulation_steps" from training arguments dictionary.

Poem

A line erased, a step set free,
No more accumulation for eval to see.
Training arguments now trimmed and neat,
The code hops forward, light on its feet.
🐇✨

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Reviewing files that changed from the base of the PR and between 0494359 and a65dbe7.

📒 Files selected for processing (1)

• src/axolotl/core/builders/causal.py (0 hunks)

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• src/axolotl/core/builders/causal.py

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

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[NanoCode012]

From reading the trainer evaluation loop, I don't think we need this config. The VRAM consumed by just an eval pass is very small compared to training, so this config would just cause unnecessary gpu->cpu movement.

However, it should not be the cause for increased vram usage as initially stated.