4a0ce3660e
### What this PR does / why we need it? Remove some parts of metrics patch, since the `cuda` hard code has been fixed by https://github.com/vllm-project/vllm/pull/14411. Signed-off-by: shen-shanshan <467638484@qq.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
# 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.
|
|
#
|
|
|
|
from typing import Callable
|
|
|
|
import torch
|
|
from vllm.spec_decode.metrics import AsyncMetricsCollector
|
|
|
|
Timer = Callable[[], float]
|
|
|
|
|
|
def _copy_rejsample_metrics_async(self) -> torch.npu.Event:
|
|
"""
|
|
TODO: torch.cuda.xxx --> torch.npu.xxx
|
|
Copy rejection/typical-acceptance sampling metrics
|
|
(number of accepted tokens, etc) to CPU asynchronously.
|
|
|
|
Returns a NPU event recording when the copy is complete.
|
|
"""
|
|
assert self._copy_stream is not None
|
|
self._copy_stream.wait_stream(torch.npu.current_stream())
|
|
|
|
with torch.npu.stream(self._copy_stream):
|
|
self._aggregate_num_accepted_tokens.copy_(
|
|
self.spec_decode_sampler.num_accepted_tokens, non_blocking=True)
|
|
self._aggregate_num_emitted_tokens.copy_(
|
|
self.spec_decode_sampler.num_emitted_tokens, non_blocking=True)
|
|
# Number of draft tokens is calculated on CPU, so no copy is
|
|
# required.
|
|
self._aggregate_num_draft_tokens = (
|
|
self.spec_decode_sampler.num_draft_tokens)
|
|
|
|
aggregate_metrics_ready = torch.npu.Event()
|
|
aggregate_metrics_ready.record(self._copy_stream)
|
|
|
|
return aggregate_metrics_ready
|
|
|
|
|
|
AsyncMetricsCollector._copy_rejsample_metrics_async = _copy_rejsample_metrics_async
|