82e17f693a
### What this PR does / why we need it? Enabling temperature in post-processing on 310P devices can cause the service to stall and eventually hang. We first traced the issue to a timeout where the temperature-related `div` operator was waiting for results from a sub-stream. After investigating the preceding operators, we finally identified the root cause as the `q.exponential_()` operator, which is not well supported on 310P and triggers an internal issue in the `add` kernel. ### Does this PR introduce _any_ user-facing change? NA ### How was this patch tested? This patch was thoroughly tested locally（accuracy-dataset test and stress test）. It is not easy to design a proper unit test for this case, and I appreciate your understanding. Signed-off-by: Tflowers-0129 <2906339855@qq.com>
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
|
|
import torch
|
|
from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant
|
|
|
|
from vllm_ascend.sample.sampler import (
|
|
DEFAULT_LOGPROBS_MODE,
|
|
AscendSampler,
|
|
AscendTopKTopPSampler,
|
|
)
|
|
from vllm_ascend.utils import global_stream, npu_stream_switch
|
|
|
|
|
|
def _random_sample_310p(
|
|
probs: torch.Tensor,
|
|
generators: dict[int, torch.Generator],
|
|
) -> torch.Tensor:
|
|
"""310P-specific random sampling with CPU exponential generation for q."""
|
|
with npu_stream_switch(global_stream()):
|
|
q = torch.empty_like(probs)
|
|
q = q.cpu()
|
|
if len(generators) != q.shape[0]:
|
|
q.exponential_()
|
|
if generators:
|
|
for i, generator in generators.items():
|
|
q[i].exponential_(generator=generator)
|
|
q = q.npu()
|
|
torch.npu.current_stream().wait_stream(global_stream())
|
|
return probs.div_(q).argmax(dim=-1).view(-1)
|
|
|
|
|
|
class AscendTopKTopPSampler310(AscendTopKTopPSampler):
|
|
def forward_native(self, logits, generators, k, p):
|
|
if vllm_is_batch_invariant():
|
|
return super().forward_native(logits, generators, k, p)
|
|
|
|
logits = self.apply_top_k_top_p(logits, k, p)
|
|
logits_to_return = None
|
|
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)
|
|
return _random_sample_310p(probs, generators), logits_to_return
|
|
|
|
|
|
class AscendSampler310(AscendSampler):
|
|
def __init__(self, logprobs_mode=DEFAULT_LOGPROBS_MODE):
|
|
super().__init__(logprobs_mode=logprobs_mode)
|
|
self.topk_topp_sampler = AscendTopKTopPSampler310(logprobs_mode=logprobs_mode)
|