Files
xc-llm-ascend/vllm_ascend/profiling_config.py
wangxiyuan b4aafd4293 [Core][Misc] Clean up ProfileExecuteDuration (#6461)
### What this PR does / why we need it?
This PR removes the custom `ProfileExecuteDuration` utility and its
usages across the codebase. This utility was used for profiling
execution duration of different stages in the inference process. It is
replaced by the standard `vllm.v1.utils.record_function_or_nullcontext`,
which integrates with PyTorch's profiler.

This change simplifies the code by removing a custom implementation in
favor of an upstream utility, improving maintainability. Associated
documentation and tests for `ProfileExecuteDuration` are also removed.

### Does this PR introduce _any_ user-facing change?
`VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` env is removed now.

### How was this patch tested?
CI passed. The changes are a cleanup and replacement with a standard
utility. Existing tests cover the functionality. The removed feature had
its own tests which are also removed.

Related RFC: #5304

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2026-02-01 20:06:01 +08:00

190 lines
6.7 KiB
Python

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