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[CI] Add standalone TIRx build, wheel, and docs pipelines
Build pinned FFI and TIRx wheels from the standalone root and run CPU, GPU, ARM, Windows, and macOS jobs. Build the Python API documentation alongside GPU tests using the built wheels, and publish only the tirx site subtree from an explicitly enabled main pipeline. Validate the archive revision and publication scope. Run the existing lint workflow on standalone branch pushes. Share environment setup, CPU detection, and release wheel builds through composite actions adapted to the standalone repository layout.
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‎.asf.yaml‎

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description: "Open ML Compiler Foundation"
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homepage: https://tvm.apache.org/
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labels:
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- tvm
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- compiler
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- tirx
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- gpu
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issues: true
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projects: true
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protected_branches:
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main:
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required_pull_request_reviews:
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required_approving_review_count: 1
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enabled_merge_buttons:
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squash: true
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squash_commit_message: PR_TITLE_AND_DESC

‎.github/actions/build-wheel-for-publish/action.yml‎

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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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name: Build TIRx Wheel
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description: >
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Build and test the standalone LLVM-enabled apache-tvm-tirx wheel for a given OS/architecture
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combination using cibuildwheel.
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inputs:
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arch:
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description: "Target architecture for cibuildwheel (e.g., x86_64, aarch64, arm64, AMD64)"
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required: true
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build:
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description: "cibuildwheel build selector (e.g., cp310-manylinux_x86_64)"
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required: true
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cmake_defines:
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description: "Feature CMake defines that vary by wheel (e.g. -DTVM_TIRX_USE_METAL=ON on macOS)"
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required: false
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default: ""
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include_cuda_runtime:
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description: "Set to 1 to build and inject the CUDA runtime library (Linux only)"
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required: false
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default: "0"
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runs:
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using: "composite"
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steps:
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# Single source of truth for the LLVM toolchain version, shared by the cache
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# key and the conda install steps below.
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- name: Set LLVM version
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shell: bash
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run: echo "LLVM_VERSION=22.1.0" >> "$GITHUB_ENV"
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- name: Prepare LLVM cache path (Unix)
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if: runner.os != 'Windows'
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shell: bash
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run: |
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set -eux
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sudo mkdir -p /opt/llvm
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sudo chown -R "$(whoami)" /opt/llvm
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# ---- Cache LLVM prefix ----
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- name: Cache LLVM
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uses: actions/cache@v5.0.5
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id: llvm-cache
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with:
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path: ${{ runner.os == 'Windows' && 'C:/opt/llvm' || '/opt/llvm' }}
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key: tirx-wheel-llvm-${{ env.LLVM_VERSION }}-${{ runner.os }}-${{ inputs.arch }}-v6
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# ---- Install LLVM via conda (cache miss only) ----
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- name: Setup conda
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if: steps.llvm-cache.outputs.cache-hit != 'true'
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uses: conda-incubator/setup-miniconda@v4.0.1
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continue-on-error: true
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id: conda1
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with:
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miniforge-version: latest
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conda-remove-defaults: true
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- name: Setup conda (retry with tar.bz2)
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if: steps.llvm-cache.outputs.cache-hit != 'true' && steps.conda1.outcome == 'failure'
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uses: conda-incubator/setup-miniconda@v4.0.1
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with:
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miniforge-version: latest
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use-only-tar-bz2: true
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conda-remove-defaults: true
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- name: Install LLVM (Unix)
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if: steps.llvm-cache.outputs.cache-hit != 'true' && runner.os != 'Windows'
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shell: bash -l {0}
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run: |
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set -eux
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if [[ "${RUNNER_OS}" == "Linux" ]]; then
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sudo mkdir -p /opt/llvm
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sudo chown -R "$(whoami)" /opt/llvm
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fi
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conda create -q -p /opt/llvm -c conda-forge \
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"llvmdev=${LLVM_VERSION}" "clangdev=${LLVM_VERSION}" "compiler-rt=${LLVM_VERSION}" zlib zstd-static libxml2-devel \
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-y
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- name: Install LLVM (Windows)
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if: steps.llvm-cache.outputs.cache-hit != 'true' && runner.os == 'Windows'
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shell: cmd /C call {0}
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run: |
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call conda create -q -p C:\opt\llvm -c conda-forge llvmdev=%LLVM_VERSION% zlib zstd-static libxml2-devel -y
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# The {project} placeholder is not expanded inside CIBW_ENVIRONMENT values, so
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# compute the forward-slash host path here (the Windows analog of the Linux
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# /project hardcode).
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- name: Compute CUDA sidecar path (Windows)
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if: runner.os == 'Windows' && inputs.include_cuda_runtime == '1'
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shell: bash
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run: echo "TVM_CUDA_EXTRA_LIB=$(cygpath -m "$(pwd)")/build-wheel-cuda/lib/tvm_runtime_cuda.dll" >> "$GITHUB_ENV"
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# ---- Build and test wheels ----
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- name: Build and test TIRx core wheel
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uses: pypa/cibuildwheel@294735312765b09d24a2fbec22660ce817587d55 # v4.1.0
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with:
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package-dir: .
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output-dir: wheelhouse
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env:
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CIBW_BUILD: ${{ inputs.build }}
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CIBW_ARCHS_LINUX: ${{ inputs.arch }}
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CIBW_ARCHS_MACOS: ${{ inputs.arch }}
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CIBW_ARCHS_WINDOWS: ${{ inputs.arch }}
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# Linux builds run in cibuildwheel's default manylinux_2_28 container;
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# bind-mount the cached LLVM prefix into it. Ignored on macOS/Windows,
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# which build without a container.
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CIBW_CONTAINER_ENGINE: "docker; create_args: --volume /opt/llvm:/opt/llvm:ro"
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# Each per-OS block carries the toolchain location (conda LLVM prefix +
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# llvm-config path), the universal static-link policy (TVM_TIRX_USE_LLVM --link-static
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# and ZLIB_USE_STATIC_LIBS), and the CUDA sidecar. Feature defines that vary by
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# wheel (e.g. TVM_TIRX_USE_METAL on macOS) come from the matrix via inputs.cmake_defines.
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# One explicit block per build platform (macOS / Linux / Windows); cibuildwheel
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# env overrides replace rather than merge, so there is no shared base block.
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CIBW_ENVIRONMENT_MACOS: >-
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CMAKE_PREFIX_PATH="/opt/llvm"
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CMAKE_ARGS="-DTVM_TIRX_USE_LLVM='/opt/llvm/bin/llvm-config --link-static' -DZLIB_USE_STATIC_LIBS=ON -DCMAKE_PREFIX_PATH=/opt/llvm ${{ inputs.cmake_defines }}"
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CIBW_ENVIRONMENT_LINUX: >-
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CMAKE_PREFIX_PATH="/opt/llvm"
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LIBRARY_PATH="/opt/llvm/lib"
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CMAKE_ARGS="-DTVM_TIRX_USE_LLVM='/opt/llvm/bin/llvm-config --link-static' -DZLIB_USE_STATIC_LIBS=ON -DCMAKE_PREFIX_PATH=/opt/llvm ${{ inputs.cmake_defines }} -DTVM_TIRX_PACKAGE_EXTRA_LIBS=/project/build-wheel-cuda/lib/libtvm_runtime_cuda.so"
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CIBW_ENVIRONMENT_WINDOWS: >-
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CMAKE_PREFIX_PATH="C:/opt/llvm/Library"
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PATH="C:/opt/llvm/Library/bin;$PATH"
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CMAKE_ARGS="-DTVM_TIRX_USE_LLVM='C:/opt/llvm/Library/bin/llvm-config.exe --link-static' -DZLIB_USE_STATIC_LIBS=ON -DCMAKE_PREFIX_PATH=C:/opt/llvm/Library ${{ inputs.cmake_defines }} -DTVM_TIRX_PACKAGE_EXTRA_LIBS=${{ env.TVM_CUDA_EXTRA_LIB }}"
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# Turns the wheel-specific assertions in
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# tests/python/runtime/test_validate_wheel_library.py
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# ON (they skip unless these are set). Every published wheel is LLVM-enabled,
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# so TVM_WHEEL_EXPECT_LLVM is always "1"; the CUDA runtime is only bundled on
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# the CUDA wheels, so TVM_WHEEL_EXPECT_CUDA_RUNTIME is "0" for CPU wheels.
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CIBW_TEST_ENVIRONMENT: >-
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TVM_WHEEL_EXPECT_LLVM="1"
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TVM_WHEEL_EXPECT_CUDA_RUNTIME="${{ inputs.include_cuda_runtime }}"

‎.github/actions/detect-env-vars/action.yml‎

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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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name: Detect Environment Variables
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description: Detects environment variables such as CPU count and sets them as outputs.
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runs:
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using: "composite"
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steps:
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- name: Run Python to detect environment variables
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shell: python
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id: detect
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run: |
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import multiprocessing, os
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output_file = open(os.environ.get("GITHUB_OUTPUT"), "a")
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def write_env_var(name, value):
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output_file.write(f"{name}={value}\n")
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print(f"Detected environment variable: {name}={value}")
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write_env_var("cpu_count", multiprocessing.cpu_count())
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outputs:
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cpu_count:
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description: "The number of CPU cores"
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value: ${{ steps.detect.outputs.cpu_count }}

‎.github/actions/setup/action.yml‎

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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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name: Setup TVM CI environment
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description: Set up the cached conda build environment and install tvm-ffi.
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runs:
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using: "composite"
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steps:
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- uses: actions/cache@v5.0.5
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env:
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CACHE_NUMBER: 2
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with:
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path: ~/conda_pkgs_dir
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key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('ci/wheel/build-environment.yaml') }}
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- uses: conda-incubator/setup-miniconda@v4.0.1
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continue-on-error: true
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id: conda1
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with:
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activate-environment: tvm-build
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channel-priority: strict
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environment-file: ci/wheel/build-environment.yaml
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auto-activate-base: false
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miniforge-version: latest
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python-version: "3.10"
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conda-remove-defaults: true
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- uses: conda-incubator/setup-miniconda@v4.0.1
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if: steps.conda1.outcome == 'failure'
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with:
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activate-environment: tvm-build
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channel-priority: strict
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environment-file: ci/wheel/build-environment.yaml
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auto-activate-base: false
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miniforge-version: latest
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use-only-tar-bz2: true
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python-version: "3.10"
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conda-remove-defaults: true
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- name: Conda info
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shell: pwsh
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run: |
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mamba info
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mamba list
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mamba info --envs
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mamba list --name base
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- name: Install tvm-ffi pip package
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shell: bash -l {0}
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run: |
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pip install -v ./3rdparty/tvm-ffi

‎.github/workflows/lint.yml‎

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on:
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push:
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branches: [main]
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branches: [main, tirx-standalone-root]
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pull_request:
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branches: [main]
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