Chunk Across Batch and Context length for logprob calculations for grpo

[pluesclues]

Relies on: unslothai/unsloth-zoo#357

[gemini-code-assist]

Summary of Changes

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This pull request significantly overhauls the log probability calculation mechanism for the GRPO trainer. The primary goal is to accurately compute logprobs by processing input sequences in smaller, manageable chunks, addressing challenges related to varying batch and context lengths, especially when dealing with padding. It also introduces specific handling for visual inputs and adjusts the application of importance sampling.

Highlights

• GRPO Logprob Calculation Refinement: Introduced a new grpo_selective_log_softmax and refactored the _get_per_token_logps_and_entropies function to process inputs in chunks, improving log probability calculations for GRPO.
• Padding Handling: Enhanced the handling of left padding by calculating and propagating max_left_pad through the logprob calculation pipeline, ensuring correct alignment for models with varying input lengths.
• TRL Importance Sampling Adjustment: Explicitly disabled TRL's default importance sampling correction logic for vLLM within the GRPO trainer.

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[gemini-code-assist]

Code Review

This pull request introduces chunking for log probability calculations in GRPO to better manage memory usage. The changes primarily involve patching GRPO trainer functions in unsloth/models/rl_replacements.py, with the core logic for batch chunking implemented in the _get_per_token_logps_and_entropies function. My review identifies a potential bug concerning tensor device placement, along with suggestions to improve code maintainability by addressing code duplication and enhancing readability in line with Python's style guidelines.