[UX] Integrate DeepGEMM into vLLM wheel via CMake (#37980)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>

.gitignore

@@ -12,6 +12,9 @@ vllm/third_party/triton_kernels/*
 # FlashMLA interface copied from source
 vllm/third_party/flashmla/flash_mla_interface.py
 
+# DeepGEMM vendored package built from source
+vllm/third_party/deep_gemm/
+
 # triton jit
 .triton
 

CMakeLists.txt

@@ -1222,6 +1222,7 @@ endif()
 
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
+    include(cmake/external_projects/deepgemm.cmake)
     include(cmake/external_projects/flashmla.cmake)
     include(cmake/external_projects/qutlass.cmake)
 

cmake/external_projects/deepgemm.cmake

@@ -0,0 +1,151 @@
+include(FetchContent)
+
+# If DEEPGEMM_SRC_DIR is set, DeepGEMM is built from that directory
+# instead of downloading.
+# It can be set as an environment variable or passed as a cmake argument.
+# The environment variable takes precedence.
+if (DEFINED ENV{DEEPGEMM_SRC_DIR})
+  set(DEEPGEMM_SRC_DIR $ENV{DEEPGEMM_SRC_DIR})
+endif()
+
+if(DEEPGEMM_SRC_DIR)
+  FetchContent_Declare(
+    deepgemm
+    SOURCE_DIR ${DEEPGEMM_SRC_DIR}
+    CONFIGURE_COMMAND ""
+    BUILD_COMMAND ""
+  )
+else()
+  # This ref should be kept in sync with tools/install_deepgemm.sh
+  FetchContent_Declare(
+    deepgemm
+    GIT_REPOSITORY https://github.com/deepseek-ai/DeepGEMM.git
+    GIT_TAG 477618cd51baffca09c4b0b87e97c03fe827ef03
+    GIT_SUBMODULES "third-party/cutlass" "third-party/fmt"
+    GIT_PROGRESS TRUE
+    CONFIGURE_COMMAND ""
+    BUILD_COMMAND ""
+  )
+endif()
+
+# Use FetchContent_Populate (not MakeAvailable) to avoid processing
+# DeepGEMM's own CMakeLists.txt which has incompatible find_package calls.
+FetchContent_GetProperties(deepgemm)
+if(NOT deepgemm_POPULATED)
+  FetchContent_Populate(deepgemm)
+endif()
+message(STATUS "DeepGEMM is available at ${deepgemm_SOURCE_DIR}")
+
+# DeepGEMM requires CUDA 12.3+ for SM90, 12.9+ for SM100
+set(DEEPGEMM_SUPPORT_ARCHS)
+if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3)
+  list(APPEND DEEPGEMM_SUPPORT_ARCHS "9.0a")
+endif()
+if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.9)
+  list(APPEND DEEPGEMM_SUPPORT_ARCHS "10.0f")
+elseif(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
+  list(APPEND DEEPGEMM_SUPPORT_ARCHS "10.0a")
+endif()
+
+cuda_archs_loose_intersection(DEEPGEMM_ARCHS
+  "${DEEPGEMM_SUPPORT_ARCHS}" "${CUDA_ARCHS}")
+
+if(DEEPGEMM_ARCHS)
+  message(STATUS "DeepGEMM CUDA architectures: ${DEEPGEMM_ARCHS}")
+
+  find_package(CUDAToolkit REQUIRED)
+
+  #
+  # Build the _C pybind11 extension from DeepGEMM's C++ source.
+  # This is a CXX-only module — CUDA kernels are JIT-compiled at runtime.
+  #
+  Python_add_library(_deep_gemm_C MODULE WITH_SOABI
+    "${deepgemm_SOURCE_DIR}/csrc/python_api.cpp")
+
+  # The pybind11 module name must be _C to match DeepGEMM's Python imports.
+  set_target_properties(_deep_gemm_C PROPERTIES OUTPUT_NAME "_C")
+
+  target_compile_definitions(_deep_gemm_C PRIVATE
+    "-DTORCH_EXTENSION_NAME=_C")
+
+  target_include_directories(_deep_gemm_C PRIVATE
+    "${deepgemm_SOURCE_DIR}/csrc"
+    "${deepgemm_SOURCE_DIR}/deep_gemm/include"
+    "${deepgemm_SOURCE_DIR}/third-party/cutlass/include"
+    "${deepgemm_SOURCE_DIR}/third-party/cutlass/tools/util/include"
+    "${deepgemm_SOURCE_DIR}/third-party/fmt/include")
+
+  target_compile_options(_deep_gemm_C PRIVATE
+    $<$<COMPILE_LANGUAGE:CXX>:-std=c++17>
+    $<$<COMPILE_LANGUAGE:CXX>:-O3>
+    $<$<COMPILE_LANGUAGE:CXX>:-Wno-psabi>
+    $<$<COMPILE_LANGUAGE:CXX>:-Wno-deprecated-declarations>)
+
+  # torch_python is required because DeepGEMM uses pybind11 type casters
+  # for at::Tensor (via PYBIND11_MODULE), unlike vLLM's own extensions which
+  # use torch::Library custom ops.
+  find_library(TORCH_PYTHON_LIBRARY torch_python
+    PATHS "${TORCH_INSTALL_PREFIX}/lib"
+    REQUIRED)
+
+  target_link_libraries(_deep_gemm_C PRIVATE
+    torch ${TORCH_LIBRARIES} "${TORCH_PYTHON_LIBRARY}"
+    CUDA::cudart CUDA::nvrtc)
+
+  # Install the shared library into the vendored package directory
+  install(TARGETS _deep_gemm_C
+    LIBRARY DESTINATION vllm/third_party/deep_gemm
+    COMPONENT _deep_gemm_C)
+
+  #
+  # Vendor DeepGEMM Python package files
+  #
+  install(FILES
+    "${deepgemm_SOURCE_DIR}/deep_gemm/__init__.py"
+    DESTINATION vllm/third_party/deep_gemm
+    COMPONENT _deep_gemm_C)
+
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/utils/"
+    DESTINATION vllm/third_party/deep_gemm/utils
+    COMPONENT _deep_gemm_C
+    FILES_MATCHING PATTERN "*.py")
+
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/testing/"
+    DESTINATION vllm/third_party/deep_gemm/testing
+    COMPONENT _deep_gemm_C
+    FILES_MATCHING PATTERN "*.py")
+
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/legacy/"
+    DESTINATION vllm/third_party/deep_gemm/legacy
+    COMPONENT _deep_gemm_C
+    FILES_MATCHING PATTERN "*.py")
+
+  # Generate envs.py (normally generated by DeepGEMM's setup.py build step)
+  file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/deep_gemm_envs.py"
+    "# Pre-installed environment variables\npersistent_envs = dict()\n")
+  install(FILES "${CMAKE_CURRENT_BINARY_DIR}/deep_gemm_envs.py"
+    DESTINATION vllm/third_party/deep_gemm
+    RENAME envs.py
+    COMPONENT _deep_gemm_C)
+
+  #
+  # Install include files needed for JIT compilation at runtime.
+  # The JIT compiler finds these relative to the package directory.
+  #
+
+  # DeepGEMM's own CUDA headers
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/deep_gemm/include/"
+    DESTINATION vllm/third_party/deep_gemm/include
+    COMPONENT _deep_gemm_C)
+
+  # CUTLASS and CuTe headers (vendored for JIT, separate from vLLM's CUTLASS)
+  install(DIRECTORY "${deepgemm_SOURCE_DIR}/third-party/cutlass/include/"
+    DESTINATION vllm/third_party/deep_gemm/include
+    COMPONENT _deep_gemm_C)
+
+else()
+  message(STATUS "DeepGEMM will not compile: "
+    "unsupported CUDA architecture ${CUDA_ARCHS}")
+  # Create empty target so setup.py doesn't fail on unsupported systems
+  add_custom_target(_deep_gemm_C)
+endif()

docker/Dockerfile

@@ -315,7 +315,7 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
 #################### CSRC BUILD IMAGE ####################
 
 #################### EXTENSIONS BUILD IMAGE ####################
-# Build DeepGEMM, DeepEP - runs in PARALLEL with csrc-build
+# Build DeepEP - runs in PARALLEL with csrc-build
 # This stage is independent and doesn't affect csrc cache
 FROM base AS extensions-build
 ARG CUDA_VERSION
@@ -327,21 +327,6 @@ ENV UV_LINK_MODE=copy
 
 WORKDIR /workspace
 
-# Build DeepGEMM wheel
-# Default moved here from tools/install_deepgemm.sh for centralized version management
-ARG DEEPGEMM_GIT_REF=477618cd51baffca09c4b0b87e97c03fe827ef03
-COPY tools/install_deepgemm.sh /tmp/install_deepgemm.sh
-RUN --mount=type=cache,target=/root/.cache/uv \
-    mkdir -p /tmp/deepgemm/dist && \
-    VLLM_DOCKER_BUILD_CONTEXT=1 TORCH_CUDA_ARCH_LIST="9.0a 10.0a" /tmp/install_deepgemm.sh \
-        --cuda-version "${CUDA_VERSION}" \
-        ${DEEPGEMM_GIT_REF:+--ref "$DEEPGEMM_GIT_REF"} \
-        --wheel-dir /tmp/deepgemm/dist || \
-    echo "DeepGEMM build skipped (CUDA version requirement not met)"
-
-# Ensure the wheel dir exists so COPY won't fail when DeepGEMM is skipped
-RUN mkdir -p /tmp/deepgemm/dist && touch /tmp/deepgemm/dist/.deepgemm_skipped
-
 # Build DeepEP wheels
 COPY tools/ep_kernels/install_python_libraries.sh /tmp/install_python_libraries.sh
 # Defaults moved here from tools/ep_kernels/install_python_libraries.sh for centralized version management
@@ -426,7 +411,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38
 
 # Copy extension wheels from extensions-build stage for later use
-COPY --from=extensions-build /tmp/deepgemm/dist /tmp/deepgemm/dist
 COPY --from=extensions-build /tmp/ep_kernels_workspace/dist /tmp/ep_kernels_workspace/dist
 
 # Check the size of the wheel if RUN_WHEEL_CHECK is true
@@ -693,15 +677,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 . /etc/environment && \
 uv pip list
 
-# Install deepgemm wheel that has been built in the `build` stage
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,from=build,source=/tmp/deepgemm/dist,target=/tmp/deepgemm/dist,ro \
-    sh -c 'if ls /tmp/deepgemm/dist/*.whl >/dev/null 2>&1; then \
-              uv pip install --system /tmp/deepgemm/dist/*.whl; \
-           else \
-              echo "No DeepGEMM wheels to install; skipping."; \
-           fi'
-
 # Pytorch now installs NVSHMEM, setting LD_LIBRARY_PATH
 ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
 

docker/versions.json

@@ -52,9 +52,6 @@
     "vllm_target_device": {
       "default": "cuda"
     },
-    "DEEPGEMM_GIT_REF": {
-      "default": "477618cd51baffca09c4b0b87e97c03fe827ef03"
-    },
     "DEEPEP_COMMIT_HASH": {
       "default": "73b6ea4"
     },

setup.py

@@ -379,6 +379,20 @@ class cmake_build_ext(build_ext):
                 dirs_exist_ok=True,
             )
 
+        if _is_cuda():
+            # copy vendored deep_gemm package from build_lib to source tree
+            # for editable installs
+            deep_gemm_build = os.path.join(
+                self.build_lib, "vllm", "third_party", "deep_gemm"
+            )
+            if os.path.exists(deep_gemm_build):
+                print(f"Copying {deep_gemm_build} to vllm/third_party/deep_gemm")
+                shutil.copytree(
+                    deep_gemm_build,
+                    "vllm/third_party/deep_gemm",
+                    dirs_exist_ok=True,
+                )
+
 
 class precompiled_build_ext(build_ext):
     """Disables extension building when using precompiled binaries."""
@@ -685,6 +699,8 @@ class precompiled_wheel_utils:
                 flashmla_regex = re.compile(
                     r"vllm/third_party/flashmla/(?:[^/.][^/]*/)*(?!\.)[^/]*\.py"
                 )
+                # DeepGEMM: extract all files (.py, .so, .cuh, .h, .hpp, etc.)
+                deep_gemm_regex = re.compile(r"vllm/third_party/deep_gemm/.*")
                 file_members = list(
                     filter(lambda x: x.filename in files_to_copy, wheel.filelist)
                 )
@@ -699,6 +715,9 @@ class precompiled_wheel_utils:
                 file_members += list(
                     filter(lambda x: flashmla_regex.match(x.filename), wheel.filelist)
                 )
+                file_members += list(
+                    filter(lambda x: deep_gemm_regex.match(x.filename), wheel.filelist)
+                )
 
                 for file in file_members:
                     print(f"[extract] {file.filename}")
@@ -987,6 +1006,12 @@ if _is_cuda():
         ext_modules.append(
             CMakeExtension(name="vllm._flashmla_extension_C", optional=True)
         )
+    if envs.VLLM_USE_PRECOMPILED or (
+        CUDA_HOME and get_nvcc_cuda_version() >= Version("12.3")
+    ):
+        # DeepGEMM requires CUDA 12.3+ (SM90/SM100)
+        # Optional since it won't build on unsupported architectures
+        ext_modules.append(CMakeExtension(name="vllm._deep_gemm_C", optional=True))
 
 if _is_cpu():
     import platform
@@ -1014,6 +1039,10 @@ package_data = {
         "entrypoints/serve/instrumentator/static/*.js",
         "entrypoints/serve/instrumentator/static/*.css",
         "distributed/kv_transfer/kv_connector/v1/hf3fs/utils/*.cpp",
+        # DeepGEMM JIT include headers (vendored via cmake)
+        "third_party/deep_gemm/include/**/*.cuh",
+        "third_party/deep_gemm/include/**/*.h",
+        "third_party/deep_gemm/include/**/*.hpp",
     ]
 }
 

tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py

@@ -14,7 +14,11 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     get_fp8_min_max,
 )
 from vllm.platforms import current_platform
-from vllm.utils.deep_gemm import DeepGemmQuantScaleFMT, has_deep_gemm
+from vllm.utils.deep_gemm import (
+    DeepGemmQuantScaleFMT,
+    has_deep_gemm,
+    transform_sf_into_required_layout,
+)
 from vllm.utils.math_utils import cdiv, round_up
 from vllm.utils.torch_utils import set_random_seed
 
@@ -256,8 +260,6 @@ def test_silu_mul_fp8_quant_deep_gemm(E: int, T: int, H: int, fp8_type: torch.dt
             and current_platform.has_device_capability(100)
             and scale_fmt == DeepGemmQuantScaleFMT.UE8M0
         ):
-            from deep_gemm import transform_sf_into_required_layout
-
             _q, _s = ref_with_scale_fmt(
                 E,
                 T,

tools/install_deepgemm.sh

@@ -5,6 +5,7 @@
 set -e
 
 # Default values
+# Keep DEEPGEMM_GIT_REF in sync with cmake/external_projects/deepgemm.cmake
 DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git"
 DEEPGEMM_GIT_REF="477618cd51baffca09c4b0b87e97c03fe827ef03"
 WHEEL_DIR=""

vllm/utils/deep_gemm.py

@@ -138,6 +138,41 @@ _get_mk_alignment_for_contiguous_layout_impl: Callable[..., Any] | None = None
 _transform_sf_into_required_layout_impl: Callable[..., Any] | None = None
 
 
+def _import_deep_gemm():
+    """Import the deep_gemm module.
+
+    Prefers an externally installed ``deep_gemm`` package (so users can
+    pin a specific version), then falls back to the vendored copy bundled
+    in the vLLM wheel.
+
+    Returns ``None`` when neither source is usable.
+    """
+    # 1. Try the external (pip-installed) package first.
+    try:
+        module = importlib.import_module("deep_gemm")
+        logger.debug_once("Imported deep_gemm module from site-packages")
+        return module
+    except ImportError:
+        logger.debug_once(
+            "deep_gemm not found in site-packages, "
+            "trying vendored vllm.third_party.deep_gemm"
+        )
+
+    # 2. Fall back to the vendored copy bundled in the vLLM wheel.
+    try:
+        module = importlib.import_module("vllm.third_party.deep_gemm")
+        logger.debug_once("Imported deep_gemm module from vllm.third_party.deep_gemm")
+        return module
+    except ImportError:
+        logger.debug_once("Vendored deep_gemm not found either")
+    except Exception as e:
+        # The vendored module may raise RuntimeError during _C.init()
+        # if JIT include files are missing (e.g. incomplete wheel).
+        logger.warning_once("Failed to import vendored deep_gemm: %s", e)
+
+    return None
+
+
 def _lazy_init() -> None:
     """Import deep_gemm and resolve symbols on first use."""
     global _fp8_gemm_nt_impl, _grouped_impl, _grouped_masked_impl
@@ -169,7 +204,9 @@ def _lazy_init() -> None:
             envs.VLLM_CACHE_ROOT, "deep_gemm"
         )
 
-    _dg = importlib.import_module("deep_gemm")
+    _dg = _import_deep_gemm()
+    if _dg is None:
+        return
 
     _fp8_gemm_nt_impl = getattr(_dg, "fp8_gemm_nt", None)
     _grouped_impl = getattr(_dg, "m_grouped_fp8_gemm_nt_contiguous", None)
@@ -193,8 +230,18 @@ def _lazy_init() -> None:
 
 def get_num_sms() -> int:
     _lazy_init()
-    _dg = importlib.import_module("deep_gemm")
-    return int(_dg.get_num_sms())
+    dg = _import_deep_gemm()
+    if dg is None:
+        raise RuntimeError("DeepGEMM is not available")
+    return int(dg.get_num_sms())
+
+
+def set_num_sms(num_sms: int) -> None:
+    _lazy_init()
+    dg = _import_deep_gemm()
+    if dg is None:
+        raise RuntimeError("DeepGEMM is not available")
+    dg.set_num_sms(num_sms)
 
 
 @functools.cache
@@ -446,6 +493,7 @@ __all__ = [
     "is_deep_gemm_e8m0_used",
     "is_deep_gemm_supported",
     "get_num_sms",
+    "set_num_sms",
     "should_use_deepgemm_for_fp8_linear",
     "get_col_major_tma_aligned_tensor",
     "get_mk_alignment_for_contiguous_layout",

vllm/utils/import_utils.py

@@ -408,8 +408,13 @@ def has_deep_ep() -> bool:
 
 
 def has_deep_gemm() -> bool:
-    """Whether the optional `deep_gemm` package is available."""
-    return _has_module("deep_gemm")
+    """Whether the optional `deep_gemm` package is available.
+
+    Prefers an externally installed ``deep_gemm`` package (so users can
+    override with a newer version), then falls back to the vendored copy
+    bundled in the vLLM wheel.
+    """
+    return _has_module("deep_gemm") or _has_module("vllm.third_party.deep_gemm")
 
 
 def has_nixl_ep() -> bool:

vllm/v1/worker/gpu_ubatch_wrapper.py

@@ -23,6 +23,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.offloader.base import get_offloader
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
+from vllm.utils.deep_gemm import set_num_sms as deep_gemm_set_num_sms
 from vllm.utils.import_utils import has_deep_gemm
 from vllm.utils.platform_utils import num_compute_units
 from vllm.v1.worker.ubatching import UBatchContext, make_ubatch_contexts
@@ -158,9 +159,7 @@ class UBatchWrapper:
         # TODO(lucas): support other kernels besides DeepGEMM
         set_compute_sms = lambda sms: None
         if has_deep_gemm() and comm_sms > 0:
-            import deep_gemm as dg
-
-            set_compute_sms = lambda sms: dg.set_num_sms(sms)
+            set_compute_sms = lambda sms: deep_gemm_set_num_sms(sms)
 
         return SMControlContextManager(
             comm_sms=comm_sms,