# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # This file is a part of the vllm-ascend project. # # This file is mainly Adapted from vllm-project/vllm/vllm/envs.py # 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. # import os 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]] = { # 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), # 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"), # Whether to compile custom kernels. If not set, the default value is True. # If set to False, the custom kernels will not be compiled. # This configuration option should only be set to False when running UT # scenarios in an environment without an NPU. Do not set it to False in # other scenarios. "COMPILE_CUSTOM_KERNELS": lambda: bool(int(os.getenv("COMPILE_CUSTOM_KERNELS", "1"))), # 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), # 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), # 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), # If set, vllm-ascend will print verbose logs during compilation "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), # 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.getenv("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), # 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"))), # 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"))), # 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)), # Whether to enable msMonitor tool to monitor the performance of vllm-ascend. "MSMONITOR_USE_DAEMON": lambda: bool(int(os.getenv("MSMONITOR_USE_DAEMON", "0"))), # Whether to enable MLAPO optimization for DeepSeek W8A8 series models. # This option is enabled by default. MLAPO can improve performance, but # it will consume more NPU memory. If reducing NPU memory usage is a higher priority # for your DeepSeek W8A8 scene, then disable it. "VLLM_ASCEND_ENABLE_MLAPO": lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_MLAPO", "1"))), # 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)), # Decide whether we should enable CP parallelism. "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(), # 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. # `dispatch_ffn_combine` can be used only for moe layer with W8A8, EP<=32, non-mtp, non-dynamic-eplb. # 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")), # Whether to anbale balance scheduling "VLLM_ASCEND_BALANCE_SCHEDULING": lambda: bool(int(os.getenv("VLLM_ASCEND_BALANCE_SCHEDULING", "0"))), # use fused op transpose_kv_cache_by_block, default is True "VLLM_ASCEND_FUSION_OP_TRANSPOSE_KV_CACHE_BY_BLOCK": lambda: bool( int(os.getenv("VLLM_ASCEND_FUSION_OP_TRANSPOSE_KV_CACHE_BY_BLOCK", "1")) ), } # end-env-vars-definition def __getattr__(name: str): # lazy evaluation of environment variables if name in env_variables: return env_variables[name]() raise AttributeError(f"module {__name__!r} has no attribute {name!r}") def __dir__(): return list(env_variables.keys())