86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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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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# This file is a part of the vllm-ascend project.
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#
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from contextlib import suppress
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from typing import Any
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import torch_npu
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from vllm.config import ProfilerConfig
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from vllm.profiler.wrapper import WorkerProfiler
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.ascend_config import get_ascend_config
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class TorchNPUProfilerWrapper(WorkerProfiler):
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"""Subclass of vLLM ``WorkerProfiler`` that wires in ``torch_npu.profiler``."""
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def __init__(self, profiler_config: ProfilerConfig, trace_name: str) -> None:
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super().__init__(profiler_config)
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self.profiler: Any = self._create_profiler(profiler_config, trace_name)
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@staticmethod
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def _create_profiler(profiler_config: ProfilerConfig, trace_name: str) -> Any:
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if profiler_config.profiler != "torch":
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raise RuntimeError(f"Unrecognized profiler: {profiler_config.profiler}")
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if not profiler_config.torch_profiler_dir:
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raise RuntimeError("torch_profiler_dir cannot be empty.")
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msmonitor_use_daemon = envs_ascend.MSMONITOR_USE_DAEMON
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with suppress(RuntimeError):
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msmonitor_use_daemon = get_ascend_config().msmonitor_use_daemon
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if msmonitor_use_daemon:
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raise RuntimeError("MSMONITOR_USE_DAEMON and torch profiler cannot be both enabled at the same time.")
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experimental_config = torch_npu.profiler._ExperimentalConfig(
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export_type=torch_npu.profiler.ExportType.Text,
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profiler_level=torch_npu.profiler.ProfilerLevel.Level1,
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msprof_tx=False,
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aic_metrics=torch_npu.profiler.AiCMetrics.PipeUtilization,
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l2_cache=False,
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op_attr=False,
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data_simplification=True,
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record_op_args=False,
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gc_detect_threshold=None,
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)
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return torch_npu.profiler.profile(
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activities=[
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torch_npu.profiler.ProfilerActivity.CPU,
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torch_npu.profiler.ProfilerActivity.NPU,
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],
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with_stack=False,
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profile_memory=profiler_config.torch_profiler_with_memory,
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# NOTE: torch_npu.profiler.with_modules is equivalent to torch.profiler.with_stack.
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# The with_stack option in torch_npu.profiler introduces significant time overhead.
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with_modules=profiler_config.torch_profiler_with_stack,
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experimental_config=experimental_config,
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on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(
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profiler_config.torch_profiler_dir,
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worker_name=trace_name,
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),
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)
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def _start(self) -> None:
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self.profiler.start()
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def _stop(self) -> None:
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self.profiler.stop()
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def _profiler_step(self) -> bool:
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return True
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