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Match CUDA_VER to TORCH_CUDA in nightly pytorch matrix
CUDA_VER in the test environment should match TORCH_CUDA in major.minor. BUILD_CUDA_VER (from build-ctk-ver input) is used for artifact names, so CUDA_VER can differ. - cu126 → CUDA_VER: 12.6.3 (was 12.9.1) - cu130 → CUDA_VER: 13.0.2 (was 13.2.1) For CUDA 12 entries, USE_BACKPORT_BINDINGS kicks in automatically since BUILD_CUDA_MAJOR (13) \!= TEST_CUDA_MAJOR (12), pulling bindings from the backport branch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Lines changed: 21 additions & 31 deletions

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‎.github/workflows/test-wheel-linux.yml‎

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@@ -284,6 +284,7 @@ jobs:
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run: setup-sanitizer
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- name: Set up test repetition on nightly runs
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if: ${{ inputs.test-mode == 'standard' }}
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run: echo "PYTEST_ADDOPTS=\"--count=${{ inputs.nruns }}\"" >> "$GITHUB_ENV"
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# ── Standard test steps (skipped for nightly modes) ──

‎.github/workflows/test-wheel-windows.yml‎

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@@ -265,6 +265,7 @@ jobs:
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cuda-version: ${{ matrix.CUDA_VER }}
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- name: Set up test repetition on nightly runs
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if: ${{ inputs.test-mode == 'standard' }}
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shell: bash --noprofile --norc -xeuo pipefail {0}
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run: echo "PYTEST_ADDOPTS=\"--count=${{ inputs.nruns }}\"" >> "$GITHUB_ENV"
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‎ci/test-matrix.yml‎

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@@ -64,10 +64,10 @@ linux:
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- { ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'rtx4090', GPU_COUNT: '1', DRIVER: 'latest', FLAVOR: 'wsl' }
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nightly:
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# nightly-pytorch (amd64 only — PyTorch does not ship arm64 GPU wheels)
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: 'latest', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: 'latest', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.6.3', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: 'latest', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.0.2', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: 'latest', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.6.3', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.0.2', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu130' }
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# nightly-numba-cuda
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- { MODE: 'nightly-numba-cuda', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest' }
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- { MODE: 'nightly-numba-cuda', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest' }
@@ -97,10 +97,10 @@ windows:
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- { ARCH: 'amd64', PY_VER: '3.14t', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'a100', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'MCDM' }
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nightly:
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# nightly-pytorch
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: 'latest', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: 'latest', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.6.3', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: 'latest', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.0.2', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: 'latest', TORCH_CUDA: 'cu130' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.6.3', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu126' }
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- { MODE: 'nightly-pytorch', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.0.2', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC', TORCH_VER: '2.9.1', TORCH_CUDA: 'cu130' }
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# nightly-numba-cuda
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- { MODE: 'nightly-numba-cuda', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '12.9.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC' }
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- { MODE: 'nightly-numba-cuda', ARCH: 'amd64', PY_VER: '3.12', CUDA_VER: '13.2.1', LOCAL_CTK: '0', GPU: 'l4', GPU_COUNT: '1', DRIVER: 'latest', DRIVER_MODE: 'TCC' }

‎ci/tools/run-tests‎

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@@ -99,27 +99,12 @@ elif [[ "${test_module}" == "nightly-pytorch" || "${test_module}" == "nightly-nu
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# Install ALL wheels (pathfinder + bindings + core) and the optional dep
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# in a single pip call so pip resolves version constraints in one shot
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# and avoids costly uninstall/reinstall cycles.
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#
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# We pushd into cuda_core/ so that --group reads test dependency groups
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# from cuda_core/pyproject.toml (needed for numpy, cupy, ml_dtypes, etc.).
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# All other wheel paths use ../ to reach the repo root.
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TEST_CUDA_MAJOR="$(cut -d '.' -f 1 <<< ${CUDA_VER})"
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CUDA_VER_MINOR="$(cut -d '.' -f 1-2 <<< "${CUDA_VER}")"
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FREE_THREADING=""
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if python -c 'import sys; assert not sys._is_gil_enabled()' 2> /dev/null; then
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FREE_THREADING+="-ft"
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fi
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# Resolve the pathfinder wheel path before pushd (it's relative to repo root).
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# CUDA_BINDINGS_ARTIFACTS_DIR and CUDA_CORE_ARTIFACTS_DIR are already absolute
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# CUDA_BINDINGS_ARTIFACTS_DIR and CUDA_CORE_ARTIFACTS_DIR are absolute
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# (set via realpath in env-vars).
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PATHFINDER_WHL=($(realpath ./cuda_pathfinder/*.whl))
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pushd ./cuda_core
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# Build wheel specs (paths are absolute, so pushd doesn't affect them)
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BINDINGS_WHL=("${CUDA_BINDINGS_ARTIFACTS_DIR}"/*.whl)
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if [[ "${LOCAL_CTK}" != 1 ]]; then
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BINDINGS_WHL=("${BINDINGS_WHL[0]}[all]")
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CORE_WHL=("${CORE_WHL[0]}[cu${TEST_CUDA_MAJOR}]")
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fi
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# All packages in one pip call: pathfinder + bindings + core + test deps + optional dep
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# All packages in one pip call: pathfinder + bindings + core + deps + optional dep.
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# We list test dependencies explicitly rather than using --group to avoid
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# the group's looser cuda-toolkit constraint (==12.*) overriding our
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# tighter pin (==12.6.*).
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PIP_ARGS=(
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"${PATHFINDER_WHL[@]}"
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./cuda_pathfinder/*.whl
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"${BINDINGS_WHL[@]}"
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"${CORE_WHL[@]}"
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--group "test-cu${TEST_CUDA_MAJOR}${FREE_THREADING}"
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pytest
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"cuda-toolkit==${CUDA_VER_MINOR}.*"
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)
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if [[ "${test_module}" == "nightly-pytorch" ]]; then
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# TORCH_VER and TORCH_CUDA must be set by the caller.
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echo "Installing pathfinder + bindings + core + test deps + PyTorch ${TORCH_VER} (${TORCH_CUDA})"
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echo "Installing pathfinder + bindings + core + PyTorch ${TORCH_VER} (${TORCH_CUDA})"
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PIP_ARGS+=(numpy ml_dtypes)
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if [[ "${TORCH_VER}" == "latest" ]]; then
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PIP_ARGS+=(torch)
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else
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PIP_ARGS+=("torch==${TORCH_VER}")
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fi
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PIP_ARGS+=(--extra-index-url "https://download.pytorch.org/whl/${TORCH_CUDA}")
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elif [[ "${test_module}" == "nightly-numba-cuda" ]]; then
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echo "Installing pathfinder + bindings + core + test deps + numba-cuda"
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PIP_ARGS+=("numba-cuda[cu${TEST_CUDA_MAJOR}]")
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echo "Installing pathfinder + bindings + core + numba-cuda"
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PIP_ARGS+=("numba-cuda[cu${TEST_CUDA_MAJOR}]" filecheck)
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fi
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pip install "${PIP_ARGS[@]}"
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popd
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echo "Nightly install complete — installed packages:"
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pip list

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