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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #2) (#5977)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| `vllm_ascend/attention/attention_mask.py` |
| `vllm_ascend/attention/attention_v1.py` |
| `vllm_ascend/attention/context_parallel/attention_cp.py` |
| `vllm_ascend/attention/context_parallel/common_cp.py` |
| `vllm_ascend/attention/context_parallel/mla_cp.py` |
| `vllm_ascend/attention/utils.py` |
| `vllm_ascend/batch_invariant.py` |
| `vllm_ascend/device/device_op.py` |
| `vllm_ascend/device_allocator/camem.py` |
| `vllm_ascend/envs.py` |


- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
This commit is contained in:
SILONG ZENG
2026-01-19 08:59:46 +08:00
committed by GitHub
parent 2b6dc100b5
commit 329961b375
11 changed files with 920 additions and 1045 deletions
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86
vllm_ascend/envs.py
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@@ -19,107 +19,89 @@
#
import os
from typing import Any, Callable, Dict
from collections.abc import Callable
from typing import Any
# The begin-* and end* here are used by the documentation generator
# to extract the used env vars.
# begin-env-vars-definition
env_variables: Dict[str, Callable[[], Any]] = {
env_variables: dict[str, Callable[[], Any]] = {
# max compile thread number for package building. Usually, it is set to
# the number of CPU cores. If not set, the default value is None, which
# means all number of CPU cores will be used.
"MAX_JOBS":
lambda: os.getenv("MAX_JOBS", None),
"MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
# The build type of the package. It can be one of the following values:
# Release, Debug, RelWithDebugInfo. If not set, the default value is Release.
"CMAKE_BUILD_TYPE":
lambda: os.getenv("CMAKE_BUILD_TYPE"),
"CMAKE_BUILD_TYPE": lambda: os.getenv("CMAKE_BUILD_TYPE"),
# The CXX compiler used for compiling the package. If not set, the default
# value is None, which means the system default CXX compiler will be used.
"CXX_COMPILER":
lambda: os.getenv("CXX_COMPILER", None),
"CXX_COMPILER": lambda: os.getenv("CXX_COMPILER", None),
# The C compiler used for compiling the package. If not set, the default
# value is None, which means the system default C compiler will be used.
"C_COMPILER":
lambda: os.getenv("C_COMPILER", None),
"C_COMPILER": lambda: os.getenv("C_COMPILER", None),
# The version of the Ascend chip. It's used for package building.
# If not set, we will query chip info through `npu-smi`.
# Please make sure that the version is correct.
"SOC_VERSION":
lambda: os.getenv("SOC_VERSION", None),
"SOC_VERSION": lambda: os.getenv("SOC_VERSION", None),
# If set, vllm-ascend will print verbose logs during compilation
"VERBOSE":
lambda: bool(int(os.getenv('VERBOSE', '0'))),
"VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
# The home path for CANN toolkit. If not set, the default value is
# /usr/local/Ascend/ascend-toolkit/latest
"ASCEND_HOME_PATH":
lambda: os.getenv("ASCEND_HOME_PATH", None),
"ASCEND_HOME_PATH": lambda: os.getenv("ASCEND_HOME_PATH", None),
# The path for HCCL library, it's used by pyhccl communicator backend. If
# not set, the default value is libhccl.so.
"HCCL_SO_PATH":
lambda: os.environ.get("HCCL_SO_PATH", None),
"HCCL_SO_PATH": lambda: os.environ.get("HCCL_SO_PATH", None),
# The version of vllm is installed. This value is used for developers who
# installed vllm from source locally. In this case, the version of vllm is
# usually changed. For example, if the version of vllm is "0.9.0", but when
# it's installed from source, the version of vllm is usually set to "0.9.1".
# In this case, developers need to set this value to "0.9.0" to make sure
# that the correct package is installed.
"VLLM_VERSION":
lambda: os.getenv("VLLM_VERSION", None),
"VLLM_VERSION": lambda: os.getenv("VLLM_VERSION", None),
# Whether to enable the model execute time observe profile. Disable it when
# running vllm ascend in production environment.
"VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE":
lambda: bool(int(os.getenv("VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE", '0'))
),
"VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE": lambda: bool(
int(os.getenv("VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE", "0"))
),
# Some models are optimized by vllm ascend. While in some case, e.g. rlhf
# training, the optimized model may not be suitable. In this case, set this
# value to False to disable the optimized model.
"USE_OPTIMIZED_MODEL":
lambda: bool(int(os.getenv('USE_OPTIMIZED_MODEL', '1'))),
"USE_OPTIMIZED_MODEL": lambda: bool(int(os.getenv("USE_OPTIMIZED_MODEL", "1"))),
# Whether to enable MatmulAllReduce fusion kernel when tensor parallel is enabled.
# this feature is supported in A2, and eager mode will get better performance.
"VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE":
lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE", '0'))),
"VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE", "0"))),
# Whether to enable FlashComm optimization when tensor parallel is enabled.
# This feature will get better performance when concurrency is large.
"VLLM_ASCEND_ENABLE_FLASHCOMM1":
lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM1", '0'))),
"VLLM_ASCEND_ENABLE_FLASHCOMM1": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM1", "0"))),
# Whether to enable FLASHCOMM2. Setting it to 0 disables the feature, while setting it to 1 or above enables it.
# The specific value set will be used as the O-matrix TP group size for flashcomm2.
# For a detailed introduction to the parameters and the differences and applicable scenarios
# between this feature and FLASHCOMM1, please refer to the feature guide in the documentation.
"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE":
lambda: int(os.getenv("VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE", 0)),
"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": lambda: int(os.getenv("VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE", 0)),
# Whether to enable MLP weight prefetch, only used in small concurrency.
"VLLM_ASCEND_ENABLE_PREFETCH_MLP":
lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_PREFETCH_MLP", '0'))),
"VLLM_ASCEND_ENABLE_PREFETCH_MLP": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_PREFETCH_MLP", "0"))),
# buffer size for gate up prefetch
"VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE":
lambda: int(
os.getenv("VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE", 18 * 1024 * 1024)),
"VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE": lambda: int(
os.getenv("VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE", 18 * 1024 * 1024)
),
# buffer size for down proj prefetch
"VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE":
lambda: int(
os.getenv("VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE", 18 * 1024 * 1024)),
"VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE": lambda: int(
os.getenv("VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE", 18 * 1024 * 1024)
),
# Whether to enable msMonitor tool to monitor the performance of vllm-ascend.
"MSMONITOR_USE_DAEMON":
lambda: bool(int(os.getenv("MSMONITOR_USE_DAEMON", '0'))),
"VLLM_ASCEND_ENABLE_MLAPO":
lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_MLAPO", '0'))),
"MSMONITOR_USE_DAEMON": lambda: bool(int(os.getenv("MSMONITOR_USE_DAEMON", "0"))),
"VLLM_ASCEND_ENABLE_MLAPO": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_MLAPO", "0"))),
# Whether to enable weight cast format to FRACTAL_NZ.
# 0: close nz;
# 1: only quant case enable nz;
# 2: enable nz as long as possible.
"VLLM_ASCEND_ENABLE_NZ":
lambda: int(os.getenv("VLLM_ASCEND_ENABLE_NZ", 1)),
"VLLM_ASCEND_ENABLE_NZ": lambda: int(os.getenv("VLLM_ASCEND_ENABLE_NZ", 1)),
# Decide whether we should enable CP parallelism.
"VLLM_ASCEND_ENABLE_CONTEXT_PARALLEL":
lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_CONTEXT_PARALLEL", '0'))),
"VLLM_ASCEND_ENABLE_CONTEXT_PARALLEL": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_CONTEXT_PARALLEL", "0"))),
# Whether to anbale dynamic EPLB
"DYNAMIC_EPLB":
lambda: os.getenv("DYNAMIC_EPLB", "false").lower(),
"DYNAMIC_EPLB": lambda: os.getenv("DYNAMIC_EPLB", "false").lower(),
# Whether to enable fused mc2(`dispatch_gmm_combine_decode`/`dispatch_ffn_combine` operator)
# 0, or not set: default ALLTOALL and MC2 will be used.
# 1: ALLTOALL and MC2 might be replaced by `dispatch_ffn_combine` operator.
@@ -127,11 +109,9 @@ env_variables: Dict[str, Callable[[], Any]] = {
# 2: MC2 might be replaced by `dispatch_gmm_combine_decode` operator.
# `dispatch_gmm_combine_decode` can be used only for **decode node** moe layer
# with W8A8. And MTP layer must be W8A8.
"VLLM_ASCEND_ENABLE_FUSED_MC2":
lambda: int(os.getenv("VLLM_ASCEND_ENABLE_FUSED_MC2", '0')),
"VLLM_ASCEND_ENABLE_FUSED_MC2": lambda: int(os.getenv("VLLM_ASCEND_ENABLE_FUSED_MC2", "0")),
# Whether to anbale balance scheduling
"VLLM_ASCEND_BALANCE_SCHEDULING":
lambda: bool(int(os.getenv("VLLM_ASCEND_BALANCE_SCHEDULING", '0'))),
"VLLM_ASCEND_BALANCE_SCHEDULING": lambda: bool(int(os.getenv("VLLM_ASCEND_BALANCE_SCHEDULING", "0"))),
}
# end-env-vars-definition