89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Callable, Optional, Union
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import torch
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import torch_npu
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from vllm.spec_decode.metrics import (AsyncMetricsCollector,
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SpecDecodeWorkerMetrics)
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Timer = Callable[[], float]
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# TODO: revert this patch when the cuda hard code is removed in vllm
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# init_tensors: Modified the hard-coded cuda judgment logic to npu;
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# maybe_collect_rejsample_metrics: Removed the check for current_platform.is_cuda_alike()
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def init_tensors(self,
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rank: int,
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device_type: Union[torch.device, str] = 'npu') -> None:
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self._rank = rank
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if isinstance(device_type, torch.device):
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device_type = device_type.type
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if device_type == 'npu':
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self._copy_stream = torch_npu.npu.Stream()
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def maybe_collect_rejsample_metrics(
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self, k: int) -> Optional[SpecDecodeWorkerMetrics]:
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# If a copy was initiated in the previous call, collect and return.
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if self._in_flight_copy is not None:
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ready_event = self._in_flight_copy
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self._in_flight_copy = None
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return self._collect_rejsample_metrics(k, ready_event)
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# Otherwise, check if we should start a new copy.
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if self._should_collect_rejsample_metrics(self._timer()):
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assert self._in_flight_copy is None
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self._in_flight_copy = self._copy_rejsample_metrics_async()
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return None
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def _copy_rejsample_metrics_async(self) -> torch.npu.Event:
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"""
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TODO: torch.cuda.xxx --> torch.npu.xxx
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Copy rejection/typical-acceptance sampling metrics
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(number of accepted tokens, etc) to CPU asynchronously.
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Returns a NPU event recording when the copy is complete.
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"""
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assert self._copy_stream is not None
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self._copy_stream.wait_stream(torch.npu.current_stream())
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with torch.npu.stream(self._copy_stream):
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self._aggregate_num_accepted_tokens.copy_(
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self.spec_decode_sampler.num_accepted_tokens, non_blocking=True)
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self._aggregate_num_emitted_tokens.copy_(
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self.spec_decode_sampler.num_emitted_tokens, non_blocking=True)
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# Number of draft tokens is calculated on CPU, so no copy is
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# required.
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self._aggregate_num_draft_tokens = (
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self.spec_decode_sampler.num_draft_tokens)
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aggregate_metrics_ready = torch.npu.Event()
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aggregate_metrics_ready.record(self._copy_stream)
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return aggregate_metrics_ready
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AsyncMetricsCollector.init_tensors = init_tensors
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AsyncMetricsCollector.maybe_collect_rejsample_metrics = maybe_collect_rejsample_metrics
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AsyncMetricsCollector._copy_rejsample_metrics_async = _copy_rejsample_metrics_async
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