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[TRTLLM-7728][perf] improve batched sampling perf for contiguous batches #7908

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merged 1 commit into from
Sep 29, 2025
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[TRTLLM-7728][perf] improve batched sampling perf for contiguous batches #7908

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15 changes: 12 additions & 3 deletions tensorrt_llm/_torch/pyexecutor/sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -373,6 +373,7 @@ def _group_requests_by_sampling_strategy(
requests: Iterable[LlmRequest],
*,
pin_memory: bool = False) -> dict[Strategy, torch.Tensor]:
# NB: Client code relies on request indices in returned torch.Tensor being sorted.
strategy_dict: dict[Strategy, list[int]] = defaultdict(list)
for req_index, req in enumerate(requests):
strategy_dict[_request_strategy(req)].append(req_index)
Expand Down Expand Up @@ -1176,12 +1177,20 @@ def _sample_batched_by_strategy(
len(speculation_group_indices), dtype=torch.int32)

group_logits_cuda_indices = logits_cuda_indexer[group_req_indices]
if group_logits_cuda_indices.numel() != logits_cuda.size(0):
# NB: Assuming that group_req_indices are sorted
group_req_1st_index, group_req_last_index = group_req_indices[
0], group_req_indices[-1]
if group_req_last_index - group_req_1st_index + 1 == len(
group_req_indices):
# Avoid data movement if indices are contiguous
group_logits_cuda = logits_cuda[
req_offsets[group_req_1st_index]:(
req_offsets[group_req_last_index] +
req_num_steps[group_req_last_index])]
else:
group_logits_cuda_indices_cuda = group_logits_cuda_indices.to(
device=logits_cuda.device, non_blocking=True)
group_logits_cuda = logits_cuda[group_logits_cuda_indices_cuda]
else:
group_logits_cuda = logits_cuda

# Indexer for accessing tokens in 'group_logits_cuda' (and 'group_next_tokens_cuda')
# corresponding to the requests in 'group_req_indices'.
Expand Down