EngineX-Ascend/enginex-ascend-910-vllm
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init v0.11.0rc0

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luopingyi
2025-10-14 10:38:28 +08:00
parent 67afd0ea78
commit 66dc16f966
278 changed files with 28130 additions and 11708 deletions
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24
vllm_ascend/sample/sampler.py
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@@ -5,11 +5,10 @@ from vllm.v1.sample.sampler import Sampler
from vllm_ascend.utils import is_310p, vllm_version_is
if not (vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1")):
if vllm_version_is("0.10.2"):
from vllm.config import LogprobsMode
DEFAULT_LOGPROBS_MODE = LogprobsMode.RAW_LOGPROBS
else:
LogprobsMode = None
DEFAULT_LOGPROBS_MODE = "raw_logprobs"
@@ -30,7 +29,8 @@ class AscendTopKTopPSampler(TopKTopPSampler):
p: torch.Tensor,
) -> torch.Tensor:
# npu_top_k_top_p uses the operator aclnnApplyTopKTopP, but aclnnApplyTopKTopP currently does not support 310P
if not is_310p() and p is not None and k is not None:
if not is_310p() and p is not None and k is not None and 1 <= int(
k.max()) <= 1024:
# npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
return torch_npu.npu_top_k_top_p(logits, p, k)
@@ -68,19 +68,19 @@ class AscendTopKTopPSampler(TopKTopPSampler):
def forward_native(self, logits, generators, k, p):
"""Override pytorch native implementation to torch_npu"""
logits = self._apply_top_k_top_p(logits, k, p)
if not (vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1")):
logits_to_return = None
logits_to_return = None
if vllm_version_is("0.10.2"):
if self.logprobs_mode == LogprobsMode.PROCESSED_LOGITS:
logits_to_return = logits
elif self.logprobs_mode == LogprobsMode.PROCESSED_LOGPROBS:
logits_to_return = logits.log_softmax(dim=-1,
dtype=torch.float32)
else:
if self.logprobs_mode == "processed_logits":
logits_to_return = logits
elif self.logprobs_mode == "processed_logprobs":
logits_to_return = logits.log_softmax(dim=-1,
dtype=torch.float32)
probs = logits.softmax(dim=-1, dtype=torch.float32)
output = None
if vllm_version_is("0.10.1.1") or vllm_version_is("0.10.1"):
output = random_sample(probs, generators)
else:
output = (random_sample(probs, generators), logits_to_return)
return output
return random_sample(probs, generators), logits_to_return