[ModelRunner V2] Share identical MTP weights#42538
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This pull request refactors the weight-sharing logic between draft and target models in EAGLE speculative decoding by introducing a _should_share utility and adding support for sharing the topk_indices_buffer. Feedback identifies a logic inconsistency in _should_share where it fails to share when the draft is None, and recommends performing tensor comparisons on the GPU to avoid the memory overhead and latency of moving data to the CPU. Additionally, it is suggested to apply the Pipeline Parallelism check to lm_head sharing to ensure consistency with the embedding sharing logic.
Signed-off-by: Nick Hill <nickhill123@gmail.com>
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yewentao256
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LGTM, thanks for the work!
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This is already done in V1 but wasn't covered in V2.
Without this, the CI fails one of the eagle tests which was tight on GPU memory.