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vllm_npu/envs.py

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+#
+# 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 typing import Any, Callable, Dict
+
+# 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. Please note that
+    # the sleep mode feature will be disabled as well if custom kernels are not
+    # compiled.
+    "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. If not set, the default value is
+    # ASCEND910B1(Available for A2 and A3 series). It's used for package building.
+    # Please make sure that the version is correct.
+    "SOC_VERSION":
+    lambda: os.getenv("SOC_VERSION", "ASCEND910B1"),
+    # 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.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),
+    # Whether to enable the trace recompiles from pytorch.
+    "vllm_npu_TRACE_RECOMPILES":
+    lambda: bool(int(os.getenv("vllm_npu_TRACE_RECOMPILES", '0'))),
+    # Whether to enable fused_experts_allgather_ep. MoeInitRoutingV3 and
+    # GroupedMatmulFinalizeRouting operators are combined to implement EP.
+    "VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP", '0'))
+                 ),
+    # Whether to enable DBO feature for deepseek model.
+    "vllm_npu_ENABLE_DBO":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_DBO", '0'))),
+    # Whether to enable the model execute time observe profile. Disable it when
+    # running vllm ascend in production environment.
+    "vllm_npu_MODEL_EXECUTE_TIME_OBSERVE":
+    lambda: bool(int(os.getenv("vllm_npu_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'))),
+    # The tolerance of the kv cache size, if the difference between the
+    # actual kv cache size and the cached kv cache size is less than this value,
+    # then the cached kv cache size will be used.
+    "vllm_npu_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE":
+    lambda: int(
+        os.getenv("vllm_npu_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE", 64)),
+    # Whether to enable the topk optimization. It's enabled by default. Please set to False if you hit any issue.
+    # We'll remove this flag in the future once it's stable enough.
+    "vllm_npu_ENABLE_TOPK_TOPP_OPTIMIZATION":
+    lambda: bool(
+        int(os.getenv("vllm_npu_ENABLE_TOPK_TOPP_OPTIMIZATION", '1'))),
+    # `LLMDataDistCMgrConnector` required variable. `DISAGGREGATED_PREFILL_RANK_TABLE_PATH` is
+    # used for llmdatadist to build the communication topology for kv cache transfer, it is
+    # a required variable if `LLMDataDistCMgrConnector` is used as kv connector for disaggregated
+    # pd. The rank table can be generated by adopting the script `gen_ranktable.sh`
+    # in vllm_npu's example folder.
+    "DISAGGREGATED_PREFILL_RANK_TABLE_PATH":
+    lambda: os.getenv("DISAGGREGATED_PREFILL_RANK_TABLE_PATH", None),
+    # `LLMDataDistCMgrConnector` required variable. `vllm_npu_LLMDD_RPC_IP` is used as the
+    # rpc communication listening ip, which will be used to receive the agent metadata from the
+    # remote worker.
+    "vllm_npu_LLMDD_RPC_IP":
+    lambda: os.getenv("vllm_npu_LLMDD_RPC_IP", "0.0.0.0"),
+    # `LLMDataDistCMgrConnector` required variable. `vllm_npu_LLMDD_RPC_PORT` is used as the
+    # rpc communication listening port, which will be used to receive the agent metadata from the
+    # remote worker.
+    "vllm_npu_LLMDD_RPC_PORT":
+    lambda: int(os.getenv("vllm_npu_LLMDD_RPC_PORT", 5557)),
+    # Whether to enable mla_pa for deepseek mla decode, this flag will be removed after its available torch_npu is public accessible
+    # and the mla_pa will be the default path of deepseek decode path.
+    "vllm_npu_MLA_PA":
+    lambda: int(os.getenv("vllm_npu_MLA_PA", 0)),
+    # 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_npu_ENABLE_MATMUL_ALLREDUCE":
+    lambda: bool(int(os.getenv("vllm_npu_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_npu_ENABLE_FLASHCOMM1":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_FLASHCOMM1", '0'))),
+    # Whether to enable MLP weight prefetch, only used in small concurrency.
+    "vllm_npu_ENABLE_PREFETCH_MLP":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_PREFETCH_MLP", '0'))),
+    # buffer size for gate up prefetch
+    "vllm_npu_MLP_GATE_UP_PREFETCH_SIZE":
+    lambda: int(
+        os.getenv("vllm_npu_MLP_GATE_UP_PREFETCH_SIZE", 18 * 1024 * 1024)),
+    # buffer size for down proj prefetch
+    "vllm_npu_MLP_DOWN_PREFETCH_SIZE":
+    lambda: int(
+        os.getenv("vllm_npu_MLP_DOWN_PREFETCH_SIZE", 18 * 1024 * 1024)),
+    # Whether to enable dense model and general optimizations for better performance.
+    # Since we modified the base parent class `linear`, this optimization is also applicable to other model types.
+    # However, there might be hidden issues, and it is currently recommended to prioritize its use with dense models.
+    "vllm_npu_ENABLE_DENSE_OPTIMIZE":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_DENSE_OPTIMIZE", '0'))),
+    # Whether to enable mlp optimize when tensor parallel is enabled.
+    # this feature in eager mode will get better performance.
+    "vllm_npu_ENABLE_MLP_OPTIMIZE":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_MLP_OPTIMIZE", '0'))),
+    # Determine the number of physical devices in a non-full-use scenario
+    # caused by the initialization of the Mooncake connector.
+    "PHYSICAL_DEVICES":
+    lambda: os.getenv("PHYSICAL_DEVICES", None),
+    # 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_npu_ENABLE_MLAPO":
+    lambda: bool(int(os.getenv("vllm_npu_ENABLE_MLAPO", '0'))),
+    # Whether to enable transpose weight and cast format to FRACTAL_NZ.
+    "vllm_npu_ENABLE_NZ":
+    lambda: int(os.getenv("vllm_npu_ENABLE_NZ", 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())