muh-bot 322f5553e1 [base/sampler] CCCL dispatch_merge_sort alias_temporaries: eliminate .repeat() allocation in _apply_penalties
Source: CCCL dispatch_merge_sort.cuh alias_temporaries() pattern
  - 4 allocations (partitions + keys + values + vsmem) packed into 1 cudaMalloc
  - Principle: never allocate throwaway intermediates in the hot path
  - dispatch_merge_sort uses ping-pong buffer to avoid copying between passes

Changes to vllm/model_executor/layers/sampler.py _apply_penalties():
  Old: repetition_penalties[:, None].repeat(1, vocab_size)
    → Creates full (num_seqs, 152064) float32 tensor = 608KB
    → Then masks most values to 1.0 (wasted allocation)
    → Then torch.where over entire vocab (wasted compute on masked positions)

  New: Broadcasting with unsqueeze(1) + conditional torch.where
    → rep_pen shape: (num_seqs, 1) broadcasts to (num_seqs, vocab_size)
    → Zero intermediate allocation
    → token_mask selects only prompt/output tokens (typically <1% of vocab)
    → Nested torch.where applies divide/multiply only where needed

Memory saving per decode step: 608KB (vocab=152064, num_seqs=1, float32)
This is in the penalties hot path that runs every decode step when
repetition_penalty != 1.0.

Also in this commit (from previous edit):
  - Fixed _sampler_cache -> _sampler_temp_storage module-level declaration
  - CCCL alias_temporaries pattern for bin_counts pre-allocation
2026-08-06 04:14:11 +00:00
2026-07-30 17:03:23 +08:00

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