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[CPU]Avoid repeated random sample compile (#28260)

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Signed-off-by: Zhang Xiangze <Xiangze.Zhang@arm.com>
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Zhang Xiangze 2025-11-07 19:03:57 +08:00 committed by GitHub
parent 315068eb4a
commit 7bdb42b2f2
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1 changed files with 10 additions and 9 deletions
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19
vllm/v1/sample/ops/topk_topp_sampler.py
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@ -127,15 +127,6 @@ class TopKTopPSampler(nn.Module):
elif self.logprobs_mode == "processed_logprobs":
logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
# Note: this is a workaround for
# https://github.com/pytorch/pytorch/pull/151218
@torch.compile(dynamic=True)
def compiled_random_sample(logits: torch.Tensor) -> torch.Tensor:
probs = logits.softmax(dim=-1, dtype=torch.float32)
q = torch.empty_like(probs)
q.exponential_()
return probs.div(q).argmax(dim=-1).view(-1)
if len(generators) != logits.shape[0]:
return compiled_random_sample(logits), logits_to_return
else:
@ -148,6 +139,16 @@ class TopKTopPSampler(nn.Module):
return probs.div_(q).argmax(dim=-1).view(-1), logits_to_return
# Note: this is a workaround for
# https://github.com/pytorch/pytorch/pull/151218
@torch.compile(dynamic=True)
def compiled_random_sample(logits: torch.Tensor) -> torch.Tensor:
probs = logits.softmax(dim=-1, dtype=torch.float32)
q = torch.empty_like(probs)
q.exponential_()
return probs.div(q).argmax(dim=-1).view(-1)
def apply_top_k_top_p(
logits: torch.Tensor,
k: torch.Tensor | None,