517fd9272d
Reverts vllm-project/vllm-ascend#4498 - vLLM version: v0.11.2 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2
208 lines
7.3 KiB
Python
208 lines
7.3 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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"""
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Service profiling configuration generator module.
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This module generates the service_profiling_symbols.yaml configuration file
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to ~/.config/vllm_ascend/ directory.
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"""
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import tempfile
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from pathlib import Path
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from typing import Optional
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import vllm
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from vllm.logger import logger
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VLLM_VERSION = vllm.__version__
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# Configuration file name
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CONFIG_FILENAME = f"service_profiling_symbols.{VLLM_VERSION}.yaml"
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# Hard-coded YAML content, default symbols changed by user can be added here.
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SERVICE_PROFILING_SYMBOLS_YAML = """
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# ===== Batch / Scheduler =====
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- symbol: vllm.v1.engine.processor:Processor.process_inputs
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.batch_hookers:process_inputs
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- symbol: vllm.v1.core.sched.scheduler:Scheduler.schedule
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.batch_hookers:schedule
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name: batchFrameworkProcessing
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- symbol: vllm_ascend.core.scheduler:AscendScheduler.schedule
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.batch_hookers:schedule
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name: batchFrameworkProcessing
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- symbol: vllm.v1.core.sched.scheduler:Scheduler._free_request
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.batch_hookers:free_request
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- symbol: vllm.v1.core.sched.scheduler:Scheduler.add_request
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.batch_hookers:add_request
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# ===== KV Cache =====
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- symbol: vllm.v1.core.kv_cache_manager:KVCacheManager.allocate_slots
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.kvcache_hookers:allocate_slots
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- symbol: vllm.v1.core.kv_cache_manager:KVCacheManager.free
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.kvcache_hookers:free
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- symbol: vllm.v1.core.kv_cache_manager:KVCacheManager.get_computed_blocks
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.kvcache_hookers:get_computed_blocks
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# ===== Model Execute =====
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- symbol: vllm.model_executor.layers.logits_processor:LogitsProcessor.forward
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.model_hookers:compute_logits
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name: computing_logits
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- symbol: vllm.v1.sample.sampler:Sampler.forward
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.model_hookers:sampler_forward
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name: sample
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- symbol: vllm.v1.executor.abstract:Executor.execute_model
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.model_hookers:execute_model
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name: modelExec
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- symbol: vllm.v1.executor.multiproc_executor:MultiprocExecutor.execute_model
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.model_hookers:execute_model
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name: modelExec
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- symbol: vllm_ascend.worker.model_runner_v1:NPUModelRunner.execute_model
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name: modelRunnerExec
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domain: ModelExecute
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- symbol: vllm_ascend.worker.model_runner_v1:NPUModelRunner._update_states
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name: _update_states
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domain: ModelExecute
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- symbol: vllm_ascend.worker.model_runner_v1:NPUModelRunner._prepare_inputs
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name: _prepare_inputs
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domain: ModelExecute
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- symbol: vllm_ascend.utils:ProfileExecuteDuration.capture_async
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.model_hookers:capture_async
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# ===== Request Lifecycle =====
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- symbol: vllm.v1.engine.async_llm:AsyncLLM.add_request
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.request_hookers:add_request_async
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- symbol: vllm.engine.async_llm_engine:AsyncLLMEngine.add_request
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.request_hookers:add_request_async
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- symbol: vllm.v1.engine.output_processor:OutputProcessor.process_outputs
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min_version: "0.9.1"
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handler: msserviceprofiler.vllm_profiler.vllm_v1.request_hookers:process_outputs
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"""
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def get_config_dir() -> Path:
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"""
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Get the vllm_ascend configuration directory path.
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Returns:
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Path: The path to ~/.config/vllm_ascend/ directory.
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"""
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home_dir = Path.home()
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config_dir = home_dir / ".config" / "vllm_ascend"
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return config_dir
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def _cleanup_temp_file(tmp_path: Optional[Path]) -> None:
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"""
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Clean up a temporary file if it exists.
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Args:
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tmp_path: Path to the temporary file to clean up.
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"""
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if tmp_path is not None and tmp_path.exists():
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try:
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tmp_path.unlink()
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except OSError:
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pass # Ignore cleanup errors
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def generate_service_profiling_config() -> Optional[Path]:
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"""
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Generate the service_profiling_symbols.yaml configuration file
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to ~/.config/vllm_ascend/ directory.
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If the configuration file already exists, this function will skip
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creating it and return the existing file path.
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If any error occurs during file creation, it will be logged but
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will not interrupt the execution. The function will return None
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to indicate that the file could not be created.
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Returns:
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Optional[Path]: The path to the generated (or existing) configuration file.
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Returns None if file creation failed.
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"""
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config_dir = get_config_dir()
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config_file = config_dir / CONFIG_FILENAME
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# Check if the configuration file already exists
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if config_file.exists():
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return config_file
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# Create the configuration directory if it doesn't exist
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try:
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config_dir.mkdir(parents=True, exist_ok=True)
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except (OSError, PermissionError) as e:
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logger.error(
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f"Failed to create configuration directory {config_dir}: {e}",
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exc_info=True)
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return None
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# Write the configuration file atomically using a temporary file
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# This ensures the file is only written if the write succeeds completely
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tmp_path = None
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try:
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# Create a temporary file in the same directory for atomic write
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with tempfile.NamedTemporaryFile(mode='w',
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encoding='utf-8',
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dir=config_dir,
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delete=False,
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suffix='.tmp',
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prefix=CONFIG_FILENAME +
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'.') as tmp_file:
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tmp_file.write(SERVICE_PROFILING_SYMBOLS_YAML)
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tmp_path = Path(tmp_file.name)
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# Atomically replace the target file with the temporary file
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tmp_path.replace(config_file)
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return config_file
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except (OSError, PermissionError) as e:
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logger.error(f"Failed to write configuration file {config_file}: {e}",
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exc_info=True)
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return None
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finally:
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# Clean up the temporary file if it wasn't successfully replaced
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_cleanup_temp_file(tmp_path)
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