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[Bugs] Fix Docs Build Problem (#97)

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* [Bugs] Docs fixed

* Update contributing.md

* Update index.md

* fix lua to text

* fix title size
This commit is contained in:
Xinyu Dong
2026-01-10 05:55:40 +08:00
committed by GitHub
parent 8c9cabd760
commit 7be26ca617
17 changed files with 721 additions and 151 deletions
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14
docs/envs.py
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@@ -47,18 +47,15 @@ env_variables: Dict[str, Callable[[], Any]] = {
# 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 Kunlun chip. If not set, the default value is
# KUNLUN910B1(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", "KUNLUN910B1"),
"SOC_VERSION": lambda: os.getenv("SOC_VERSION", "KUNLUNP800"),
# If set, vllm-kunlun 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/Kunlun/kunlun-toolkit/latest
"KUNLUN_HOME_PATH": lambda: os.getenv("KUNLUN_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 path for XCCL library, it's used by pyxccl communicator backend. If
# not set, the default value is libxccl.so。
"XCCL_SO_PATH": lambda: os.environ.get("XCCL_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
@@ -119,7 +116,6 @@ env_variables: Dict[str, Callable[[], Any]] = {
# and the mla_pa will be the default path of deepseek decode path.
"VLLM_KUNLUN_MLA_PA": lambda: int(os.getenv("VLLM_KUNLUN_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_KUNLUN_ENABLE_MATMUL_ALLREDUCE": lambda: bool(
int(os.getenv("VLLM_KUNLUN_ENABLE_MATMUL_ALLREDUCE", "0"))
),