init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/profiler/__init__.py

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+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+#
+
+from vllm_ascend.profiler.torch_npu_profiler import TorchNPUProfilerWrapper
+
+__all__ = ["TorchNPUProfilerWrapper"]

vllm_ascend/profiler/torch_npu_profiler.py

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