chore: import upstream snapshot with attribution
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wehub-resource-sync
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<!--- Licensed to the Apache Software Foundation (ASF) under one -->
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<!--- or more contributor license agreements. See the NOTICE file -->
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<!--- distributed with this work for additional information -->
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<!--- regarding copyright ownership. The ASF licenses this file -->
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<!--- to you under the Apache License, Version 2.0 (the -->
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<!--- "License"); you may not use this file except in compliance -->
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<!--- with the License. You may obtain a copy of the License at -->
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<!--- http://www.apache.org/licenses/LICENSE-2.0 -->
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<!--- Unless required by applicable law or agreed to in writing, -->
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<!--- software distributed under the License is distributed on an -->
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<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
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<!--- KIND, either express or implied. See the License for the -->
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<!--- specific language governing permissions and limitations -->
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<!--- under the License. -->
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# TVM wheel packaging
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The wheels are built by a standard `cibuildwheel` flow, configured in
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`.github/workflows/publish_wheel.yml` and `pyproject.toml` (`[tool.cibuildwheel]`
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and `[tool.scikit-build]`). This directory holds the few helper scripts that flow
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invokes:
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- `manylinux_build_libtvm_runtime_cuda.sh` — run by the `build_cuda_runtime` CI
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stage; builds the `libtvm_runtime_cuda.so` sidecar inside the prebuilt
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`quay.io/manylinux_cuda` image (CUDA toolkit preinstalled).
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- `windows_build_libtvm_runtime_cuda.bat` — the Windows equivalent (run with
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`shell: cmd`); installs the CUDA toolkit via conda and builds
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`tvm_runtime_cuda.dll`.
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- `build-environment.yaml` — conda environment for building the wheel.
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the specific
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# language governing permissions and limitations under the License.
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# Build environment for TVM wheel building.
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# This environment provides the necessary dependencies for building TVM wheels.
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name: tvm-wheel-build
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# The conda channels to lookup the dependencies
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channels:
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- conda-forge
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# The packages to install to the environment
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dependencies:
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# Core build tools
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- cmake >=3.24
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- ninja
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- make
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- llvmdev >=11
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- python >=3.10
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- pip
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- git
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- bzip2
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- zlib
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- zstd-static
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- pytest
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- numpy
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- scipy
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- cython
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- libxml2-devel
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#!/usr/bin/env bash
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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#
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# Build libtvm_runtime_cuda.so inside a manylinux CUDA container, run by the
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# build_cuda_runtime CI job. The official quay.io/manylinux_cuda images ship
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# the CUDA toolkit preinstalled under /usr/local/cuda, but omit the libcuda
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# driver stub, so we install just cuda-driver-devel from the image's CUDA repo.
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# Builds the sidecar into build-wheel-cuda/lib/ for the wheel build to bundle.
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#
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# Usage: manylinux_build_libtvm_runtime_cuda.sh
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set -euxo pipefail
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repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/../../.." && pwd)"
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build_dir="${repo_root}/build-wheel-cuda"
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parallel="$(getconf _NPROCESSORS_ONLN 2>/dev/null || echo 4)"
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# The image ships the toolkit but not the libcuda driver stub; install it from
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# the image's CUDA repo so the sidecar links libcuda.so.1 (sync version with tag).
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dnf -y install cuda-driver-devel-13-1
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# Build the CUDA runtime sidecar with CUDA on and LLVM off, so it does not need
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# the LLVM prefix; the main CPU wheel links LLVM statically. The manylinux CUDA
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# image already ships cmake and make, and the build uses the default Makefiles
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# generator (no Ninja), so no build tools are installed here. Put the bundled
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# CPython and CUDA toolchain on PATH for the CMake configure and nvcc.
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export PATH="/opt/python/cp310-cp310/bin:/usr/local/cuda/bin:${PATH}"
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nvcc --version
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rm -rf "${build_dir}"
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# CMAKE_CUDA_COMPILER only tells CMake which nvcc to use; it does not affect the
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# resulting libtvm_runtime_cuda.so, which is built only from .cc host sources (no
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# .cu device code, so nvcc is never invoked for it). CMAKE_CUDA_ARCHITECTURES is
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# intentionally not set: it would be a no-op here for the same reason (verified --
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# the .so is byte-identical across arch values and carries no device code), and
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# modern CMake fills in a default so configure does not fail without it.
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cmake -S "${repo_root}" -B "${build_dir}" \
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-DCMAKE_BUILD_TYPE=Release \
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-DBUILD_TESTING=OFF \
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-DTVM_BUILD_PYTHON_MODULE=ON \
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-DUSE_CUDA=/usr/local/cuda \
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-DUSE_LLVM=OFF \
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-DUSE_CUBLAS=OFF -DUSE_CUDNN=OFF -DUSE_CUTLASS=OFF -DUSE_NCCL=OFF -DUSE_NVTX=OFF \
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-DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc
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cmake --build "${build_dir}" --target tvm_runtime tvm_runtime_cuda --parallel "${parallel}"
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cuda_lib="${build_dir}/lib/libtvm_runtime_cuda.so"
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test -f "${cuda_lib}"
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patchelf --set-rpath '$ORIGIN' "${cuda_lib}"
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echo "CUDA runtime: ${cuda_lib}"
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@echo off
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rem Licensed to the Apache Software Foundation (ASF) under one
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rem or more contributor license agreements. See the NOTICE file
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rem distributed with this work for additional information
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rem regarding copyright ownership. The ASF licenses this file
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rem to you under the Apache License, Version 2.0 (the
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rem "License"); you may not use this file except in compliance
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rem with the License. You may obtain a copy of the License at
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rem
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rem http://www.apache.org/licenses/LICENSE-2.0
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rem
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rem Unless required by applicable law or agreed to in writing,
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rem software distributed under the License is distributed on an
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rem "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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rem KIND, either express or implied. See the License for the
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rem specific language governing permissions and limitations
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rem under the License.
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rem
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rem Build tvm_runtime_cuda.dll on a Windows runner, run by the build_cuda_runtime
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rem CI job (on the host; unlike Linux there is no build container on Windows).
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rem Installs the pinned CUDA toolkit via conda and builds the sidecar into
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rem build-wheel-cuda\lib\ for the wheel build to bundle. Windows mirror of
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rem manylinux_build_libtvm_runtime_cuda.sh. Run with: shell: cmd.
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setlocal enableextensions
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rem repo root = this script's directory / ..\..\.. (native Windows paths; no cygpath).
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pushd "%~dp0..\..\.." || exit /b 1
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set "repo_root=%CD%"
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popd
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set "build_dir=%repo_root%\build-wheel-cuda"
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set "cuda_prefix=C:\opt\cuda"
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rem Locate conda: the runner ships Miniconda (exposed via %CONDA%) but it may not be
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rem on PATH in this shell.
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set "conda_exe=conda"
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where conda >nul 2>nul || set "conda_exe=%CONDA%\Scripts\conda.exe"
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rem Install the pinned CUDA toolkit via conda from the nvidia channel, mirroring the
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rem LLVM-via-conda install used elsewhere. The win-64 channel caps at 13.0.x, so this
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rem pins 13.0.2 -- slightly behind the Linux image's CUDA 13.1, which is harmless: the
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rem sidecar has no device code and links the CUDA runtime by soname only. The nvidia
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rem CDN occasionally returns a transient HTTP 5xx, so retry once; a half-finished first
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rem attempt can leave the prefix partially populated, so wipe it before retrying.
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if not exist "%cuda_prefix%\Library\bin\nvcc.exe" (
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call "%conda_exe%" create -q -p "%cuda_prefix%" -c nvidia/label/cuda-13.0.2 cuda-toolkit -y
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if errorlevel 1 (
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if exist "%cuda_prefix%" rmdir /s /q "%cuda_prefix%"
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call "%conda_exe%" create -q -p "%cuda_prefix%" -c nvidia/label/cuda-13.0.2 cuda-toolkit -y || exit /b 1
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)
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)
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rem conda lays the Windows toolkit out under <prefix>\Library (bin\nvcc.exe,
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rem lib\x64\cudart.lib, include\...). Discover the root from nvcc.exe so TVM's
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rem FindCUDA MSVC branch resolves against the real layout instead of a hardcode.
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set "nvcc_exe="
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for /f "delims=" %%i in ('dir /s /b "%cuda_prefix%\nvcc.exe" 2^>nul') do if not defined nvcc_exe set "nvcc_exe=%%i"
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if not defined nvcc_exe ( echo nvcc.exe not found under %cuda_prefix% & exit /b 1 )
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rem cuda_root = dirname(dirname(nvcc)) = <prefix>\Library
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for %%i in ("%nvcc_exe%") do set "nvcc_bin=%%~dpi"
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pushd "%nvcc_bin%.." || exit /b 1
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set "cuda_root=%CD%"
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popd
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set "CUDA_PATH=%cuda_root%"
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python -m pip install -U pip cmake ninja || exit /b 1
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"%nvcc_exe%" --version || exit /b 1
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rem nvcc needs the MSVC host compiler (cl.exe), so locate VS via vswhere and run the
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rem cmake configure+build inside vcvars64 (this shell is not a VS Developer prompt).
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set "vswhere=C:\Program Files (x86)\Microsoft Visual Studio\Installer\vswhere.exe"
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set "vs_path="
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for /f "usebackq delims=" %%i in (`"%vswhere%" -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath`) do set "vs_path=%%i"
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if not defined vs_path ( echo Visual Studio with VC tools not found & exit /b 1 )
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set "vcvars=%vs_path%\VC\Auxiliary\Build\vcvars64.bat"
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if exist "%build_dir%" rmdir /s /q "%build_dir%"
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rem CMAKE_CUDA_COMPILER only tells CMake which nvcc to use (load-bearing: the conda
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rem nvcc is not on PATH); it does not affect the resulting tvm_runtime_cuda.dll, which
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rem is built only from .cc host sources (no .cu device code). CMAKE_CUDA_ARCHITECTURES
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rem is intentionally not set -- a no-op for the same reason, and modern CMake fills a
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rem default. -allow-unsupported-compiler guards against the runner's MSVC being newer
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rem than the CUDA toolkit officially supports. The cmake command is kept on one line:
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rem `^` continuations in a batch file break on any trailing whitespace.
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rem CMake parses backslashes in string values as escapes (e.g. C:\opt -> invalid \o),
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rem so hand cmake forward-slash paths. cmd builtins (rmdir / if exist) keep backslashes.
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set "repo_root_fwd=%repo_root:\=/%"
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set "build_dir_fwd=%build_dir:\=/%"
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set "cuda_root_fwd=%cuda_root:\=/%"
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set "nvcc_fwd=%nvcc_exe:\=/%"
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call "%vcvars%" || exit /b 1
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cmake -S "%repo_root_fwd%" -B "%build_dir_fwd%" -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTING=OFF -DTVM_BUILD_PYTHON_MODULE=ON -DUSE_CUDA="%cuda_root_fwd%" -DUSE_LLVM=OFF -DUSE_CUBLAS=OFF -DUSE_CUDNN=OFF -DUSE_CUTLASS=OFF -DUSE_NCCL=OFF -DUSE_NVTX=OFF -DCMAKE_CUDA_COMPILER="%nvcc_fwd%" -DCMAKE_CUDA_FLAGS="-allow-unsupported-compiler" || exit /b 1
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cmake --build "%build_dir_fwd%" --target tvm_runtime tvm_runtime_cuda --config Release || exit /b 1
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if not exist "%build_dir%\lib\tvm_runtime_cuda.dll" ( echo tvm_runtime_cuda.dll was not produced & exit /b 1 )
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rem No patchelf/rpath step on Windows; delvewheel vendors dependencies at repair time.
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echo CUDA runtime: %build_dir%\lib\tvm_runtime_cuda.dll
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endlocal
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