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1 Commits
ciflow/ind
...
revert-cpp
| Author | SHA1 | Date | |
|---|---|---|---|
| 2eacbe792a |
@ -49,20 +49,12 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
|
||||
export SYSROOT_DEP="sysroot_linux-64=2.17"
|
||||
fi
|
||||
|
||||
# Install correct Python version
|
||||
# Also ensure sysroot is using a modern GLIBC to match system compilers
|
||||
if [ "$ANACONDA_PYTHON_VERSION" = "3.14" ]; then
|
||||
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
|
||||
python="3.14.0" \
|
||||
${SYSROOT_DEP} \
|
||||
-c conda-forge
|
||||
else
|
||||
# Install correct Python version
|
||||
# Also ensure sysroot is using a modern GLIBC to match system compilers
|
||||
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
|
||||
python="$ANACONDA_PYTHON_VERSION" \
|
||||
${SYSROOT_DEP}
|
||||
fi
|
||||
|
||||
# libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
|
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# which is provided in libstdcxx 12 and up.
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conda_install libstdcxx-ng=12.3.0 --update-deps -c conda-forge
|
||||
|
||||
@ -10,7 +10,7 @@ else
|
||||
arch_path='sbsa'
|
||||
fi
|
||||
|
||||
NVSHMEM_VERSION=3.4.5
|
||||
NVSHMEM_VERSION=3.3.24
|
||||
|
||||
function install_cuda {
|
||||
version=$1
|
||||
|
||||
@ -40,7 +40,11 @@ EOF
|
||||
|
||||
# Default url values
|
||||
rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
|
||||
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
|
||||
|
||||
# Add amdgpu repository
|
||||
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
|
||||
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
|
||||
|
||||
# Add rocm repository
|
||||
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
|
||||
|
||||
@ -138,12 +138,10 @@ numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
|
||||
#test_binary_ufuncs.py
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||||
numpy==1.22.4; python_version == "3.10"
|
||||
numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
|
||||
numpy==2.1.2; python_version >= "3.13" and python_version < "3.14"
|
||||
numpy==2.3.4; python_version >= "3.14"
|
||||
numpy==2.1.2; python_version >= "3.13"
|
||||
|
||||
pandas==2.0.3; python_version < "3.13"
|
||||
pandas==2.2.3; python_version >= "3.13" and python_version < "3.14"
|
||||
pandas==2.3.3; python_version >= "3.14"
|
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pandas==2.2.3; python_version >= "3.13"
|
||||
|
||||
#onnxruntime
|
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#Description: scoring engine for Open Neural Network Exchange (ONNX) models
|
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@ -155,8 +153,7 @@ opt-einsum==3.3
|
||||
#Pinned versions: 3.3
|
||||
#test that import: test_linalg.py
|
||||
|
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optree==0.13.0 ; python_version < "3.14"
|
||||
optree==0.17.0 ; python_version >= "3.14"
|
||||
optree==0.13.0
|
||||
#Description: A library for tree manipulation
|
||||
#Pinned versions: 0.13.0
|
||||
#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
|
||||
@ -255,8 +252,7 @@ scikit-image==0.22.0
|
||||
#test that import:
|
||||
|
||||
scipy==1.10.1 ; python_version <= "3.11"
|
||||
scipy==1.14.1 ; python_version > "3.11" and python_version < "3.14"
|
||||
scipy==1.16.2 ; python_version >= "3.14"
|
||||
scipy==1.14.1 ; python_version >= "3.12"
|
||||
# Pin SciPy because of failing distribution tests (see #60347)
|
||||
#Description: scientific python
|
||||
#Pinned versions: 1.10.1
|
||||
@ -328,8 +324,7 @@ pywavelets==1.7.0 ; python_version >= "3.12"
|
||||
#Pinned versions: 1.4.1
|
||||
#test that import:
|
||||
|
||||
lxml==5.3.0 ; python_version < "3.14"
|
||||
lxml==6.0.2 ; python_version >= "3.14"
|
||||
lxml==5.3.0
|
||||
#Description: This is a requirement of unittest-xml-reporting
|
||||
|
||||
PyGithub==2.3.0
|
||||
@ -339,9 +334,7 @@ sympy==1.13.3
|
||||
#Pinned versions:
|
||||
#test that import:
|
||||
|
||||
onnx==1.19.1 ; python_version < "3.14"
|
||||
# Unpin once Python 3.14 is supported. See onnxruntime issue 26309.
|
||||
onnx==1.18.0 ; python_version == "3.14"
|
||||
onnx==1.19.1
|
||||
#Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
|
||||
#Pinned versions:
|
||||
#test that import:
|
||||
@ -366,7 +359,7 @@ pwlf==2.2.1
|
||||
#test that import: test_sac_estimator.py
|
||||
|
||||
# To build PyTorch itself
|
||||
pyyaml==6.0.3
|
||||
pyyaml==6.0.2
|
||||
pyzstd
|
||||
setuptools==78.1.1
|
||||
packaging==23.1
|
||||
|
||||
@ -100,8 +100,6 @@ COPY ./common/common_utils.sh common_utils.sh
|
||||
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
|
||||
COPY ci_commit_pins/timm.txt timm.txt
|
||||
COPY ci_commit_pins/torchbench.txt torchbench.txt
|
||||
# Only build aoti cpp tests when INDUCTOR_BENCHMARKS is set to True
|
||||
ENV BUILD_AOT_INDUCTOR_TEST ${INDUCTOR_BENCHMARKS}
|
||||
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
|
||||
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
|
||||
|
||||
|
||||
@ -6,7 +6,7 @@ dependencies = [
|
||||
"GitPython==3.1.45",
|
||||
"docker==7.1.0",
|
||||
"pytest==7.3.2",
|
||||
"uv==0.9.6"
|
||||
"uv==0.9.5"
|
||||
]
|
||||
|
||||
[tool.setuptools]
|
||||
|
||||
@ -460,18 +460,28 @@ test_inductor_shard() {
|
||||
--verbose
|
||||
}
|
||||
|
||||
test_inductor_aoti_cpp() {
|
||||
test_inductor_aoti() {
|
||||
# docker build uses bdist_wheel which does not work with test_aot_inductor
|
||||
# TODO: need a faster way to build
|
||||
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
|
||||
# We need to hipify before building again
|
||||
python3 tools/amd_build/build_amd.py
|
||||
fi
|
||||
if [[ "$BUILD_ENVIRONMENT" == *sm86* ]]; then
|
||||
BUILD_COMMAND=(TORCH_CUDA_ARCH_LIST=8.6 USE_FLASH_ATTENTION=OFF python -m pip install --no-build-isolation -v -e .)
|
||||
# TODO: Replace me completely, as one should not use conda libstdc++, nor need special path to TORCH_LIB
|
||||
TEST_ENVS=(CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="/opt/conda/envs/py_3.10/lib:${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}")
|
||||
else
|
||||
BUILD_COMMAND=(python -m pip install --no-build-isolation -v -e .)
|
||||
TEST_ENVS=(CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}")
|
||||
fi
|
||||
|
||||
# aoti cmake custom command requires `torch` to be installed
|
||||
# initialize the cmake build cache and install torch
|
||||
/usr/bin/env "${BUILD_COMMAND[@]}"
|
||||
# rebuild with the build cache with `BUILD_AOT_INDUCTOR_TEST` enabled
|
||||
/usr/bin/env CMAKE_FRESH=1 BUILD_AOT_INDUCTOR_TEST=1 "${BUILD_COMMAND[@]}"
|
||||
|
||||
/usr/bin/env "${TEST_ENVS[@]}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference cpp/test_vec_half_AVX2 -dist=loadfile
|
||||
}
|
||||
|
||||
@ -1766,7 +1776,7 @@ elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper* ]]; then
|
||||
install_torchvision
|
||||
PYTHONPATH=/torchbench test_inductor_cpp_wrapper_shard "$SHARD_NUMBER"
|
||||
if [[ "$SHARD_NUMBER" -eq "1" ]]; then
|
||||
test_inductor_aoti_cpp
|
||||
test_inductor_aoti
|
||||
fi
|
||||
elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
|
||||
install_torchvision
|
||||
|
||||
@ -7,9 +7,12 @@ if "%DESIRED_PYTHON%" == "3.13t" (
|
||||
set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.13.0/python-3.13.0-amd64.exe"
|
||||
set ADDITIONAL_OPTIONS="Include_freethreaded=1"
|
||||
set PYTHON_EXEC="python3.13t"
|
||||
) else if "%DESIRED_PYTHON%"=="3.14" (
|
||||
echo Python version is set to 3.14 or 3.14t
|
||||
set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0rc1-amd64.exe"
|
||||
) else if "%DESIRED_PYTHON%"=="3.14t" (
|
||||
echo Python version is set to 3.14 or 3.14t
|
||||
set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0-amd64.exe"
|
||||
set "PYTHON_INSTALLER_URL=https://www.python.org/ftp/python/3.14.0/python-3.14.0rc1-amd64.exe"
|
||||
set ADDITIONAL_OPTIONS="Include_freethreaded=1"
|
||||
set PYTHON_EXEC="python3.14t"
|
||||
) else (
|
||||
|
||||
4
.github/actions/diskspace-cleanup/action.yml
vendored
4
.github/actions/diskspace-cleanup/action.yml
vendored
@ -27,9 +27,7 @@ runs:
|
||||
docker system prune -af
|
||||
diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
|
||||
if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then
|
||||
diskspace_cutoff_int=$((diskspace_cutoff + 0))
|
||||
difference=$((100 - diskspace_cutoff_int))
|
||||
echo "Error: Available diskspace is less than $difference percent. Not enough diskspace."
|
||||
echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace."
|
||||
echo "$msg"
|
||||
exit 1
|
||||
else
|
||||
|
||||
2
.github/ci_commit_pins/audio.txt
vendored
2
.github/ci_commit_pins/audio.txt
vendored
@ -1 +1 @@
|
||||
3b0e7a6f192ca2715e7e6cbe5db007aea7165fe2
|
||||
69bbe7363897764f9e758d851cd0340147d27f94
|
||||
|
||||
2
.github/ci_commit_pins/vision.txt
vendored
2
.github/ci_commit_pins/vision.txt
vendored
@ -1 +1 @@
|
||||
218d2ab791d437309f91e0486eb9fa7f00badc17
|
||||
1752fe6809b74921644866275ab80244b96e80bc
|
||||
|
||||
20
.github/merge_rules.yaml
vendored
20
.github/merge_rules.yaml
vendored
@ -540,26 +540,6 @@
|
||||
- Lint
|
||||
- pull
|
||||
|
||||
- name: PrivateUse1
|
||||
patterns:
|
||||
- torch/accelerator/**
|
||||
- torch/utils/backend_registration.py
|
||||
- torch/csrc/acc/**
|
||||
- torch/csrc/DeviceAccelerator.*
|
||||
- torch/csrc/profiler/standalone/privateuse1_observer.*
|
||||
- aten/src/ATen/DeviceAccelerator.*
|
||||
- aten/src/ATen/core/GeneratorForPrivateuseone.*
|
||||
- aten/src/ATen/detail/PrivateUse1HooksInterface.*
|
||||
- docs/source/accelerator/**
|
||||
- test/cpp_extensions/open_registration_extension/torch_openreg/**
|
||||
approved_by:
|
||||
- albanD
|
||||
- fffrog
|
||||
mandatory_checks_name:
|
||||
- EasyCLA
|
||||
- Lint
|
||||
- pull
|
||||
|
||||
- name: superuser
|
||||
patterns:
|
||||
- '*'
|
||||
|
||||
1
.github/pytorch-probot.yml
vendored
1
.github/pytorch-probot.yml
vendored
@ -26,7 +26,6 @@ ciflow_push_tags:
|
||||
- ciflow/nightly
|
||||
- ciflow/op-benchmark
|
||||
- ciflow/periodic
|
||||
- ciflow/periodic-rocm-mi200
|
||||
- ciflow/periodic-rocm-mi300
|
||||
- ciflow/pull
|
||||
- ciflow/quantization-periodic
|
||||
|
||||
@ -56,7 +56,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
"nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | "
|
||||
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
|
||||
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
|
||||
"nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | "
|
||||
"nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | "
|
||||
"nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'"
|
||||
@ -73,7 +73,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
"nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | "
|
||||
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
|
||||
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
|
||||
"nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | "
|
||||
"nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | "
|
||||
"nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
|
||||
@ -90,7 +90,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
"nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | "
|
||||
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
|
||||
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
|
||||
"nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | "
|
||||
"nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | "
|
||||
"nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'"
|
||||
@ -107,7 +107,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
|
||||
"nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | "
|
||||
"nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | "
|
||||
"nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | "
|
||||
"nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | "
|
||||
"nvidia-nvtx==13.0.85; platform_system == 'Linux' | "
|
||||
"nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | "
|
||||
"nvidia-cufile==1.15.1.6; platform_system == 'Linux'"
|
||||
|
||||
1
.github/workflows/docker-builds.yml
vendored
1
.github/workflows/docker-builds.yml
vendored
@ -57,7 +57,6 @@ jobs:
|
||||
pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11,
|
||||
pytorch-linux-jammy-py3.10-clang12,
|
||||
pytorch-linux-jammy-py3.13-clang12,
|
||||
pytorch-linux-jammy-py3.14-clang12,
|
||||
pytorch-linux-jammy-rocm-n-py3,
|
||||
pytorch-linux-noble-rocm-n-py3,
|
||||
pytorch-linux-jammy-rocm-n-py3-benchmarks,
|
||||
|
||||
56
.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml
generated
vendored
56
.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml
generated
vendored
@ -132,7 +132,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_10-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -178,7 +178,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_10-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -224,7 +224,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_10-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -270,7 +270,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_10-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -381,7 +381,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_11-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -427,7 +427,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_11-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -473,7 +473,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_11-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -519,7 +519,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_11-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -630,7 +630,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_12-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -676,7 +676,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_12-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -722,7 +722,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_12-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -768,7 +768,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_12-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -879,7 +879,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -925,7 +925,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -971,7 +971,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1017,7 +1017,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1128,7 +1128,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13t-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1174,7 +1174,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13t-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1220,7 +1220,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13t-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1266,7 +1266,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_13t-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1377,7 +1377,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1423,7 +1423,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1469,7 +1469,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1515,7 +1515,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1626,7 +1626,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14t-cuda-aarch64-12_6
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1672,7 +1672,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14t-cuda-aarch64-12_8
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1718,7 +1718,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14t-cuda-aarch64-12_9
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
@ -1764,7 +1764,7 @@ jobs:
|
||||
ALPINE_IMAGE: "arm64v8/alpine"
|
||||
build_name: manywheel-py3_14t-cuda-aarch64-13_0
|
||||
build_environment: linux-aarch64-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
timeout-minutes: 420
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
56
.github/workflows/generated-linux-binary-manywheel-nightly.yml
generated
vendored
56
.github/workflows/generated-linux-binary-manywheel-nightly.yml
generated
vendored
@ -127,7 +127,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_10-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_10-cuda12_6-test: # Testing
|
||||
@ -193,7 +193,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_10-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_10-cuda12_8-test: # Testing
|
||||
@ -259,7 +259,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_10-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_10-cuda12_9-test: # Testing
|
||||
@ -325,7 +325,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_10-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_10-cuda13_0-test: # Testing
|
||||
@ -793,7 +793,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_11-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_11-cuda12_6-test: # Testing
|
||||
@ -859,7 +859,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_11-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_11-cuda12_8-test: # Testing
|
||||
@ -925,7 +925,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_11-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_11-cuda12_9-test: # Testing
|
||||
@ -991,7 +991,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_11-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_11-cuda13_0-test: # Testing
|
||||
@ -1459,7 +1459,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_12-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_12-cuda12_6-test: # Testing
|
||||
@ -1525,7 +1525,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_12-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_12-cuda12_8-test: # Testing
|
||||
@ -1591,7 +1591,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_12-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_12-cuda12_9-test: # Testing
|
||||
@ -1657,7 +1657,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_12-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_12-cuda13_0-test: # Testing
|
||||
@ -2125,7 +2125,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13-cuda12_6-test: # Testing
|
||||
@ -2191,7 +2191,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13-cuda12_8-test: # Testing
|
||||
@ -2257,7 +2257,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13-cuda12_9-test: # Testing
|
||||
@ -2323,7 +2323,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13-cuda13_0-test: # Testing
|
||||
@ -2791,7 +2791,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13t-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13t-cuda12_6-test: # Testing
|
||||
@ -2857,7 +2857,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13t-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13t-cuda12_8-test: # Testing
|
||||
@ -2923,7 +2923,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13t-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13t-cuda12_9-test: # Testing
|
||||
@ -2989,7 +2989,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_13t-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_13t-cuda13_0-test: # Testing
|
||||
@ -3457,7 +3457,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14-cuda12_6-test: # Testing
|
||||
@ -3523,7 +3523,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14-cuda12_8-test: # Testing
|
||||
@ -3589,7 +3589,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14-cuda12_9-test: # Testing
|
||||
@ -3655,7 +3655,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14-cuda13_0-test: # Testing
|
||||
@ -4123,7 +4123,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14t-cuda12_6
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.6.77; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.6.80; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.6.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.0.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.7.77; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14t-cuda12_6-test: # Testing
|
||||
@ -4189,7 +4189,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14t-cuda12_8
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.8.93; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.8.90; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.8.90; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.8.4.1; platform_system == 'Linux' | nvidia-cufft-cu12==11.3.3.83; platform_system == 'Linux' | nvidia-curand-cu12==10.3.9.90; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14t-cuda12_8-test: # Testing
|
||||
@ -4255,7 +4255,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14t-cuda12_9
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.4.5; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14t-cuda12_9-test: # Testing
|
||||
@ -4321,7 +4321,7 @@ jobs:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build_name: manywheel-py3_14t-cuda13_0
|
||||
build_environment: linux-binary-manywheel
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.4.5; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc==13.0.88; platform_system == 'Linux' | nvidia-cuda-runtime==13.0.96; platform_system == 'Linux' | nvidia-cuda-cupti==13.0.85; platform_system == 'Linux' | nvidia-cudnn-cu13==9.13.0.50; platform_system == 'Linux' | nvidia-cublas==13.1.0.3; platform_system == 'Linux' | nvidia-cufft==12.0.0.61; platform_system == 'Linux' | nvidia-curand==10.4.0.35; platform_system == 'Linux' | nvidia-cusolver==12.0.4.66; platform_system == 'Linux' | nvidia-cusparse==12.6.3.3; platform_system == 'Linux' | nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | nvidia-nvtx==13.0.85; platform_system == 'Linux' | nvidia-nvjitlink==13.0.88; platform_system == 'Linux' | nvidia-cufile==1.15.1.6; platform_system == 'Linux'
|
||||
secrets:
|
||||
github-token: ${{ secrets.GITHUB_TOKEN }}
|
||||
manywheel-py3_14t-cuda13_0-test: # Testing
|
||||
|
||||
84
.github/workflows/periodic-rocm-mi200.yml
vendored
84
.github/workflows/periodic-rocm-mi200.yml
vendored
@ -1,84 +0,0 @@
|
||||
name: periodic-rocm-mi200
|
||||
|
||||
on:
|
||||
schedule:
|
||||
# We have several schedules so jobs can check github.event.schedule to activate only for a fraction of the runs.
|
||||
# Also run less frequently on weekends.
|
||||
- cron: 45 0,8,16 * * 1-5
|
||||
- cron: 45 4 * * 0,6
|
||||
- cron: 45 4,12,20 * * 1-5
|
||||
- cron: 45 12 * * 0,6
|
||||
- cron: 29 8 * * * # about 1:29am PDT, for mem leak check and rerun disabled tests
|
||||
push:
|
||||
tags:
|
||||
- ciflow/periodic/*
|
||||
- ciflow/periodic-rocm-mi200/*
|
||||
branches:
|
||||
- release/*
|
||||
workflow_dispatch:
|
||||
|
||||
concurrency:
|
||||
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}-${{ github.event.schedule }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
llm-td:
|
||||
if: github.repository_owner == 'pytorch'
|
||||
name: before-test
|
||||
uses: ./.github/workflows/llm_td_retrieval.yml
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
|
||||
target-determination:
|
||||
name: before-test
|
||||
uses: ./.github/workflows/target_determination.yml
|
||||
needs: llm-td
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
|
||||
get-label-type:
|
||||
name: get-label-type
|
||||
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
|
||||
if: (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch'
|
||||
with:
|
||||
triggering_actor: ${{ github.triggering_actor }}
|
||||
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
|
||||
curr_branch: ${{ github.head_ref || github.ref_name }}
|
||||
curr_ref_type: ${{ github.ref_type }}
|
||||
|
||||
linux-jammy-rocm-py3_10-build:
|
||||
name: linux-jammy-rocm-py3.10
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build-environment: linux-jammy-rocm-py3.10
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
{ config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
{ config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-rocm-py3_10-test:
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
name: linux-jammy-rocm-py3.10
|
||||
uses: ./.github/workflows/_rocm-test.yml
|
||||
needs:
|
||||
- linux-jammy-rocm-py3_10-build
|
||||
- target-determination
|
||||
with:
|
||||
build-environment: linux-jammy-rocm-py3.10
|
||||
docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
|
||||
secrets: inherit
|
||||
31
.github/workflows/periodic.yml
vendored
31
.github/workflows/periodic.yml
vendored
@ -204,6 +204,37 @@ jobs:
|
||||
test-matrix: ${{ needs.linux-jammy-cuda13_0-py3_10-gcc11-build.outputs.test-matrix }}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-rocm-py3_10-build:
|
||||
name: linux-jammy-rocm-py3.10
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
needs: get-label-type
|
||||
with:
|
||||
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
|
||||
build-environment: linux-jammy-rocm-py3.10
|
||||
docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "distributed", shard: 1, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
{ config: "distributed", shard: 2, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
{ config: "distributed", shard: 3, num_shards: 3, runner: "linux.rocm.gpu.mi250.4", owners: ["module:rocm", "oncall:distributed"] },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-rocm-py3_10-test:
|
||||
permissions:
|
||||
id-token: write
|
||||
contents: read
|
||||
name: linux-jammy-rocm-py3.10
|
||||
uses: ./.github/workflows/_rocm-test.yml
|
||||
needs:
|
||||
- linux-jammy-rocm-py3_10-build
|
||||
- target-determination
|
||||
with:
|
||||
build-environment: linux-jammy-rocm-py3.10
|
||||
docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
|
||||
test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
|
||||
secrets: inherit
|
||||
|
||||
linux-jammy-cuda12_8-py3-gcc11-slow-gradcheck-build:
|
||||
name: linux-jammy-cuda12.8-py3-gcc11-slow-gradcheck
|
||||
uses: ./.github/workflows/_linux-build.yml
|
||||
|
||||
1
.github/workflows/upload-test-stats.yml
vendored
1
.github/workflows/upload-test-stats.yml
vendored
@ -6,7 +6,6 @@ on:
|
||||
- pull
|
||||
- trunk
|
||||
- periodic
|
||||
- periodic-rocm-mi200
|
||||
- periodic-rocm-mi300
|
||||
- inductor
|
||||
- unstable
|
||||
|
||||
20
.github/workflows/xpu.yml
vendored
20
.github/workflows/xpu.yml
vendored
@ -59,18 +59,14 @@ jobs:
|
||||
runner: linux.c7i.12xlarge
|
||||
test-matrix: |
|
||||
{ include: [
|
||||
{ config: "default", shard: 1, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 2, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 3, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 4, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 5, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 6, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 7, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 8, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 9, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 10, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 11, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 12, num_shards: 12, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 1, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 2, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 3, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 4, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 5, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 6, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 7, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
{ config: "default", shard: 8, num_shards: 8, runner: "linux.idc.xpu" },
|
||||
]}
|
||||
secrets: inherit
|
||||
|
||||
|
||||
@ -1198,6 +1198,12 @@ exclude_patterns = [
|
||||
'torch/_inductor/fx_passes/serialized_patterns/**',
|
||||
'torch/_inductor/autoheuristic/artifacts/**',
|
||||
'torch/utils/model_dump/preact.mjs',
|
||||
# These files are all grandfathered in, feel free to remove from this list
|
||||
# as necessary
|
||||
# NOTE: remove the patterns in the order they are listed
|
||||
'aten/src/ATen/native/[a-pA-P]*/**',
|
||||
'aten/src/ATen/[a-mA-M]*/**',
|
||||
'test/**',
|
||||
]
|
||||
init_command = [
|
||||
'python3',
|
||||
|
||||
@ -374,7 +374,7 @@ cmake_dependent_option(
|
||||
"Build the lazy Torchscript backend, not compatible with mobile builds" ON
|
||||
"NOT INTERN_BUILD_MOBILE" OFF)
|
||||
cmake_dependent_option(BUILD_FUNCTORCH "Build Functorch" ON "BUILD_PYTHON" OFF)
|
||||
cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin folder"
|
||||
cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin fodler"
|
||||
OFF "USE_CUDA" OFF)
|
||||
cmake_dependent_option(USE_KLEIDIAI "Use KleidiAI for the ARM CPU & AARCH64 architecture." ON
|
||||
"CPU_AARCH64" OFF)
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
|
||||
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
@ -72,7 +72,7 @@ Elaborating Further:
|
||||
|
||||
If you use NumPy, then you have used Tensors (a.k.a. ndarray).
|
||||
|
||||
|
||||
|
||||
|
||||
PyTorch provides Tensors that can live either on the CPU or the GPU and accelerates the
|
||||
computation by a huge amount.
|
||||
@ -99,7 +99,7 @@ from several research papers on this topic, as well as current and past work suc
|
||||
While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
|
||||
You get the best of speed and flexibility for your crazy research.
|
||||
|
||||
|
||||
|
||||
|
||||
### Python First
|
||||
|
||||
|
||||
@ -260,7 +260,7 @@ IF(USE_FBGEMM_GENAI)
|
||||
if(USE_CUDA)
|
||||
# To avoid increasing the build time/binary size unnecessarily, use an allow-list of kernels to build.
|
||||
# If you want to integrate a kernel from FBGEMM into torch, you have to add it here.
|
||||
set(FBGEMM_CUTLASS_KERNELS_REGEX ".*(mx8mx8bf16_grouped|f4f4bf16_grouped).*")
|
||||
set(FBGEMM_CUTLASS_KERNELS_REGEX ".*mx8mx8bf16_grouped.*")
|
||||
file(GLOB_RECURSE fbgemm_genai_native_cuda_cu
|
||||
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/*.cu"
|
||||
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/**/*.cu")
|
||||
@ -291,7 +291,6 @@ IF(USE_FBGEMM_GENAI)
|
||||
|
||||
set(fbgemm_genai_cuh
|
||||
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
|
||||
"${FBGEMM_GENAI_SRCS}/cutlass_extensions/f4f4bf16_grouped/"
|
||||
"${FBGEMM_GENAI_SRCS}/"
|
||||
)
|
||||
|
||||
|
||||
@ -677,8 +677,8 @@ struct CachingHostAllocatorImpl {
|
||||
// size. This allows us to quickly find a free block of the right size.
|
||||
// We use deque to store per size free list and guard the list with its own
|
||||
// mutex.
|
||||
alignas(hardware_destructive_interference_size) std::vector<FreeBlockList<B>>
|
||||
free_list_{MAX_SIZE_INDEX};
|
||||
alignas(hardware_destructive_interference_size) std::vector<FreeBlockList<B>> free_list_ =
|
||||
std::vector<FreeBlockList<B>>(MAX_SIZE_INDEX);
|
||||
|
||||
alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
|
||||
std::deque<std::pair<E, B*>> events_; // event queue paired with block
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
|
||||
namespace at {
|
||||
|
||||
// Redeclaring 'DimVector' type and size inside 'at' namespace.
|
||||
// Re-declaring 'DimVector' type and size inside 'at' namespace.
|
||||
// This is done to avoid modifying every use into their 'c10'
|
||||
// equivalent.
|
||||
|
||||
|
||||
@ -16,7 +16,7 @@ _GeneratorRegister::_GeneratorRegister(const GeneratorFuncType& func) {
|
||||
|
||||
TORCH_WARN_DEPRECATION(
|
||||
"REGISTER_GENERATOR_PRIVATEUSE1 is deprecated. \
|
||||
Please derive PrivateUse1HooksInterface to implement getNewGenerator instead.")
|
||||
Please derive PrivateUse1HooksInterface to implememt getNewGenerator instead.")
|
||||
|
||||
TORCH_CHECK(
|
||||
!GetGeneratorPrivate().has_value(),
|
||||
|
||||
@ -149,7 +149,7 @@
|
||||
* First, keep in mind that we assume that boxed containers will
|
||||
* have to deal with `IValue` (e.g. `c10::List`). In this context,
|
||||
* what may be happening is that `IValue` doesn't store internally
|
||||
* your type `T`. Instead, it constructs a type new `T` every time
|
||||
* your type `T`. Instead, it constructs a type new `T` everytime
|
||||
* you try to get `T` for it (see `IListRef<at::OptinalTensorRef>`).
|
||||
*/
|
||||
|
||||
@ -186,7 +186,7 @@ class IListRef;
|
||||
* This macro is useful because it allows us to handle different
|
||||
* types (that correspond to different tags) to be implemented
|
||||
* only once. We can do it even when the implementation of the
|
||||
* different tags aren't syntactically the same, by dispatching
|
||||
* different tags aren't syntatically the same, by dispatching
|
||||
* it to a function (e.g. `ImplT::<dispatch-function>(this_)`).
|
||||
*/
|
||||
#define TORCH_ILISTREF_UNWRAP(TAG, BODY) \
|
||||
|
||||
@ -42,7 +42,7 @@ class IListRefTagImplBase<IListRefTag::Unboxed, T, ListElemT> {
|
||||
/*
|
||||
* We have these function (besides the `unwrap`s above) because the
|
||||
* implementation for both `IListRef::operator[]` and `IListRefIterator::operator*`
|
||||
* weren't syntactically equal for the existing tags at the time
|
||||
* weren't syntatically equal for the existing tags at the time
|
||||
* (`Unboxed` and `Boxed`).
|
||||
*/
|
||||
static IListRefConstRef<T> front(const list_type& lst) {
|
||||
|
||||
@ -12,7 +12,7 @@ namespace at {
|
||||
// in order. This is most commonly used in autogenerated code,
|
||||
// where it is convenient to have a function that can uniformly
|
||||
// take arguments of different types. If your arguments
|
||||
// are homogeneous consider using a std::initializer_list instead.
|
||||
// are homogenous consider using a std::initializer_list instead.
|
||||
//
|
||||
// For examples of this in use, see torch/csrc/utils/variadic.h
|
||||
template <typename F>
|
||||
|
||||
@ -111,7 +111,7 @@ void Dispatcher::waitForDef(const FunctionSchema& schema) {
|
||||
TORCH_INTERNAL_ASSERT(r,
|
||||
"Expected main interpreter to define ", schema.operator_name(),
|
||||
", but this didn't happen within timeout. Are you trying to load "
|
||||
"different models in the same torchdeploy/multipy instance? You " // codespell:ignore
|
||||
"different models in the same torchdeploy/multipy instance? You "
|
||||
"must warmup each interpreter identically, e.g., import all "
|
||||
"the same dependencies.");
|
||||
}
|
||||
@ -129,7 +129,7 @@ void Dispatcher::waitForImpl(const OperatorName& op_name, std::optional<c10::Dis
|
||||
TORCH_INTERNAL_ASSERT(r,
|
||||
"Expected main interpreter to implement ", dk, " for ", op_name,
|
||||
", but this didn't happen within timeout. Are you trying to load "
|
||||
"different models in the same torchdeploy/multipy instance? You " // codespell:ignore
|
||||
"different models in the same torchdeploy/multipy instance? You "
|
||||
"must warmup each interpreter identically, e.g., import all "
|
||||
"the same dependencies.");
|
||||
}
|
||||
@ -442,8 +442,8 @@ RegistrationHandleRAII Dispatcher::registerFallback(DispatchKey dispatchKey, Ker
|
||||
|
||||
auto idx = getDispatchTableIndexForDispatchKey(dispatchKey);
|
||||
TORCH_CHECK(idx >= 0 && static_cast<uint64_t>(idx) < backendFallbackKernels_.size(), "idx=", idx);
|
||||
// NB: Preserve BC for registering fallback for AutogradPrivateUse1 multiple time,
|
||||
// refer to https://github.com/pytorch/pytorch/issues/163979 for more information.
|
||||
// NB: Perserve BC for registering fallback for AutogradPrivateUse1 multiple time,
|
||||
// refer to https://github.com/pytorch/pytorch/issues/163979 for more informations.
|
||||
TORCH_CHECK(
|
||||
dispatchKey == DispatchKey::AutogradPrivateUse1 ||
|
||||
!backendFallbackKernels_[idx].kernel.isValid(),
|
||||
|
||||
@ -222,8 +222,7 @@ class TORCH_API Dispatcher final {
|
||||
return backendFallbackKernels_[dispatch_ix].kernel.isValid();
|
||||
}
|
||||
|
||||
// Used by torchdeploy/multipy for multiple // codespell:ignore: multipy
|
||||
// interpreters racing.
|
||||
// Used by torchdeploy/multipy for multiple interpreters racing.
|
||||
void waitForDef(const FunctionSchema& schema);
|
||||
void waitForImpl(
|
||||
const OperatorName& op_name,
|
||||
@ -415,7 +414,7 @@ class TORCH_API Dispatcher final {
|
||||
std::unique_ptr<detail::RegistrationListenerList> listeners_;
|
||||
|
||||
// This condition variable gets notified whenever we add a new def/impl to the
|
||||
// dispatch table. This is primarily used by multiply/torchdeploy, when
|
||||
// dispatch table. This is primarily used by multipy/torchdeploy, when
|
||||
// we have multiple interpreters trying to register to the dispatch table.
|
||||
// In this situation, whenever the non-primary interpreter would have tried
|
||||
// to register to the dispatch table, instead it will check to see if the
|
||||
|
||||
@ -990,7 +990,7 @@ struct C10_EXPORT ivalue::Future final : c10::intrusive_ptr_target {
|
||||
std::unique_lock<std::mutex> lock(mutex_);
|
||||
if (completed_) {
|
||||
// This should be rare and shouldn't cause log spew. Its important to
|
||||
// log errors and that's why we have this log here.
|
||||
// log errors and thats why we have this log here.
|
||||
std::string msg = c10::str(
|
||||
"Skipping setting following error on the Future since "
|
||||
"it is already marked completed (this is not necessarily "
|
||||
|
||||
@ -887,7 +887,7 @@ struct TORCH_API ListType
|
||||
// this function will return the global singleton type pointer
|
||||
// the type List<T>.
|
||||
// The extra "identifier" argument is needed because we have multiple container types
|
||||
// that all reuse this function (List<T>, array<T, N>, etc.)
|
||||
// that all re-use this function (List<T>, array<T, N>, etc.)
|
||||
static TypePtr get(const std::string& identifier, TypePtr inner);
|
||||
|
||||
// common cast List[Tensor]
|
||||
@ -983,7 +983,7 @@ struct TORCH_API DictType : public SharedType {
|
||||
// this function will return the global singleton type pointer
|
||||
// the type List<T>.
|
||||
// The extra "identifier" argument is needed because we have multiple container types
|
||||
// that all reuse this function (Dict<K, V> and unordered_map<K, V>)
|
||||
// that all re-use this function (Dict<K, V> and unordered_map<K, V>)
|
||||
static TypePtr get(const std::string& identifier, TypePtr key, TypePtr val);
|
||||
|
||||
private:
|
||||
|
||||
@ -680,7 +680,7 @@ TORCH_API bool elementTypeCanBeInferredFromMembers(const TypePtr& elem_type) {
|
||||
return false;
|
||||
}
|
||||
if (elem_type->kind() == AnyType::Kind) {
|
||||
// List of Any can contains heterogeneous types
|
||||
// List of Any can contains heterogenous types
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
||||
@ -354,9 +354,47 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
|
||||
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(abs)
|
||||
Vectorized frac() const;
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(trunc)
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(sqrt)
|
||||
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
Vectorized<c10::BFloat16> neg() const {
|
||||
return -values;
|
||||
}
|
||||
Vectorized<c10::BFloat16> reciprocal() const {
|
||||
return 1.0f / values;
|
||||
}
|
||||
Vectorized<c10::BFloat16> operator==(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values == other.values;
|
||||
}
|
||||
|
||||
Vectorized<c10::BFloat16> operator!=(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values != other.values;
|
||||
}
|
||||
|
||||
Vectorized<c10::BFloat16> operator<(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values < other.values;
|
||||
}
|
||||
|
||||
Vectorized<c10::BFloat16> operator<=(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values <= other.values;
|
||||
}
|
||||
|
||||
Vectorized<c10::BFloat16> operator>(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values > other.values;
|
||||
}
|
||||
|
||||
Vectorized<c10::BFloat16> operator>=(
|
||||
const Vectorized<c10::BFloat16>& other) const {
|
||||
return values >= other.values;
|
||||
}
|
||||
#else
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
|
||||
DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(reciprocal)
|
||||
DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator==)
|
||||
DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator!=)
|
||||
@ -364,6 +402,7 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
|
||||
DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator<=)
|
||||
DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>)
|
||||
DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>=)
|
||||
#endif
|
||||
|
||||
#undef DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
|
||||
#undef DEFINE_BINARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
|
||||
@ -412,28 +451,52 @@ template <>
|
||||
Vectorized<c10::BFloat16> inline operator+(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
return x + y;
|
||||
#else
|
||||
return binary_operator_via_float(std::plus<Vectorized<float>>(), a, b);
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<c10::BFloat16> inline operator-(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
return x - y;
|
||||
#else
|
||||
return binary_operator_via_float(std::minus<Vectorized<float>>(), a, b);
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<c10::BFloat16> inline operator*(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
return x * y;
|
||||
#else
|
||||
return binary_operator_via_float(std::multiplies<Vectorized<float>>(), a, b);
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
Vectorized<c10::BFloat16> inline operator/(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
return x / y;
|
||||
#else
|
||||
return binary_operator_via_float(std::divides<Vectorized<float>>(), a, b);
|
||||
#endif
|
||||
}
|
||||
|
||||
// frac. Implement this here so we can use subtraction
|
||||
@ -544,12 +607,19 @@ Vectorized<c10::BFloat16> inline fmadd(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b,
|
||||
const Vectorized<c10::BFloat16>& c) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
bfloat16x8_t z = c;
|
||||
return x * y + z;
|
||||
#else
|
||||
// NOTE [BF16 FMA]: There isn't an FMA that accumulates into BF16! Also,
|
||||
// vbfmlalbq_f32 and vbfmlaltq_f32 take the even and odd-numbered
|
||||
// elements, not the bottom and top half, so they don't seem
|
||||
// particularly useful here. Ideally we would include dot product in
|
||||
// the Vectorized interface...
|
||||
return a * b + c;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
@ -557,8 +627,15 @@ Vectorized<c10::BFloat16> inline fnmadd(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b,
|
||||
const Vectorized<c10::BFloat16>& c) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
bfloat16x8_t z = c;
|
||||
return (-x) * y + z;
|
||||
#else
|
||||
// See NOTE [BF16 FMA] above.
|
||||
return -a * b + c;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
@ -566,8 +643,15 @@ Vectorized<c10::BFloat16> inline fmsub(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b,
|
||||
const Vectorized<c10::BFloat16>& c) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
bfloat16x8_t z = c;
|
||||
return x * y - z;
|
||||
#else
|
||||
// See NOTE [BF16 FMA] above.
|
||||
return a * b - c;
|
||||
#endif
|
||||
}
|
||||
|
||||
template <>
|
||||
@ -575,8 +659,15 @@ Vectorized<c10::BFloat16> inline fnmsub(
|
||||
const Vectorized<c10::BFloat16>& a,
|
||||
const Vectorized<c10::BFloat16>& b,
|
||||
const Vectorized<c10::BFloat16>& c) {
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
bfloat16x8_t x = a;
|
||||
bfloat16x8_t y = b;
|
||||
bfloat16x8_t z = c;
|
||||
return (-x) * y - z;
|
||||
#else
|
||||
// See NOTE [BF16 FMA] above.
|
||||
return -a * b - c;
|
||||
#endif
|
||||
}
|
||||
|
||||
#endif // !defined(C10_MOBILE) && defined(__aarch64__)
|
||||
|
||||
@ -21,46 +21,12 @@ inline void convertImpl(
|
||||
}
|
||||
}
|
||||
|
||||
template <typename to_type>
|
||||
inline void convertFromBool(
|
||||
const bool* __restrict src,
|
||||
to_type* __restrict dst,
|
||||
int64_t n) {
|
||||
const uint8_t* srcPtr = reinterpret_cast<const uint8_t*>(src);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
dst[i] = srcPtr[i] != 0 ? static_cast<to_type>(1) : static_cast<to_type>(0);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename from_type>
|
||||
inline void convertToBool(
|
||||
const from_type* __restrict src,
|
||||
bool* __restrict dst,
|
||||
int64_t n) {
|
||||
uint8_t* dstPtr = reinterpret_cast<uint8_t*>(dst);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
dstPtr[i] = src[i] != static_cast<from_type>(0) ? 1 : 0;
|
||||
}
|
||||
}
|
||||
|
||||
#define CONVERT_TEMPLATE(from_type, to_type) \
|
||||
template <> \
|
||||
inline void convert(const from_type* src, to_type* dst, int64_t n) { \
|
||||
return convertImpl<from_type, to_type>(src, dst, n); \
|
||||
}
|
||||
|
||||
#define CONVERT_FROM_BOOL_TEMPLATE(to_type) \
|
||||
inline void convert(const bool* src, to_type* dst, int64_t n) { \
|
||||
return convertFromBool<to_type>(src, dst, n); \
|
||||
}
|
||||
|
||||
#define CONVERT_TO_BOOL_TEMPLATE(from_type) \
|
||||
inline void convert(const from_type* src, bool* dst, int64_t n) { \
|
||||
return convertToBool<from_type>(src, dst, n); \
|
||||
}
|
||||
|
||||
CONVERT_TEMPLATE(uint8_t, uint8_t)
|
||||
CONVERT_TEMPLATE(uint8_t, int8_t)
|
||||
CONVERT_TEMPLATE(uint8_t, int16_t)
|
||||
@ -68,7 +34,6 @@ CONVERT_TEMPLATE(uint8_t, int32_t)
|
||||
CONVERT_TEMPLATE(uint8_t, int64_t)
|
||||
CONVERT_TEMPLATE(uint8_t, float)
|
||||
CONVERT_TEMPLATE(uint8_t, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(uint8_t)
|
||||
CONVERT_TEMPLATE(int8_t, uint8_t)
|
||||
CONVERT_TEMPLATE(int8_t, int8_t)
|
||||
CONVERT_TEMPLATE(int8_t, int16_t)
|
||||
@ -76,7 +41,6 @@ CONVERT_TEMPLATE(int8_t, int32_t)
|
||||
CONVERT_TEMPLATE(int8_t, int64_t)
|
||||
CONVERT_TEMPLATE(int8_t, float)
|
||||
CONVERT_TEMPLATE(int8_t, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(int8_t)
|
||||
CONVERT_TEMPLATE(int16_t, uint8_t)
|
||||
CONVERT_TEMPLATE(int16_t, int8_t)
|
||||
CONVERT_TEMPLATE(int16_t, int16_t)
|
||||
@ -84,7 +48,6 @@ CONVERT_TEMPLATE(int16_t, int32_t)
|
||||
CONVERT_TEMPLATE(int16_t, int64_t)
|
||||
CONVERT_TEMPLATE(int16_t, float)
|
||||
CONVERT_TEMPLATE(int16_t, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(int16_t)
|
||||
CONVERT_TEMPLATE(int32_t, uint8_t)
|
||||
CONVERT_TEMPLATE(int32_t, int8_t)
|
||||
CONVERT_TEMPLATE(int32_t, int16_t)
|
||||
@ -92,7 +55,6 @@ CONVERT_TEMPLATE(int32_t, int32_t)
|
||||
CONVERT_TEMPLATE(int32_t, int64_t)
|
||||
CONVERT_TEMPLATE(int32_t, float)
|
||||
CONVERT_TEMPLATE(int32_t, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(int32_t)
|
||||
CONVERT_TEMPLATE(int64_t, uint8_t)
|
||||
CONVERT_TEMPLATE(int64_t, int8_t)
|
||||
CONVERT_TEMPLATE(int64_t, int16_t)
|
||||
@ -100,7 +62,6 @@ CONVERT_TEMPLATE(int64_t, int32_t)
|
||||
CONVERT_TEMPLATE(int64_t, int64_t)
|
||||
CONVERT_TEMPLATE(int64_t, float)
|
||||
CONVERT_TEMPLATE(int64_t, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(int64_t)
|
||||
CONVERT_TEMPLATE(float, uint8_t)
|
||||
CONVERT_TEMPLATE(float, int8_t)
|
||||
CONVERT_TEMPLATE(float, int16_t)
|
||||
@ -108,7 +69,6 @@ CONVERT_TEMPLATE(float, int32_t)
|
||||
CONVERT_TEMPLATE(float, int64_t)
|
||||
CONVERT_TEMPLATE(float, float)
|
||||
CONVERT_TEMPLATE(float, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(float)
|
||||
CONVERT_TEMPLATE(double, uint8_t)
|
||||
CONVERT_TEMPLATE(double, int8_t)
|
||||
CONVERT_TEMPLATE(double, int16_t)
|
||||
@ -116,14 +76,6 @@ CONVERT_TEMPLATE(double, int32_t)
|
||||
CONVERT_TEMPLATE(double, int64_t)
|
||||
CONVERT_TEMPLATE(double, float)
|
||||
CONVERT_TEMPLATE(double, double)
|
||||
CONVERT_TO_BOOL_TEMPLATE(double)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(uint8_t)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(int8_t)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(int16_t)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(int32_t)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(int64_t)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(float)
|
||||
CONVERT_FROM_BOOL_TEMPLATE(double)
|
||||
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
|
||||
|
||||
#define CONVERT_FROM_FP16_TEMPLATE(to_type) \
|
||||
@ -155,41 +107,6 @@ CONVERT_TO_FP16_TEMPLATE(int32_t)
|
||||
CONVERT_TO_FP16_TEMPLATE(int64_t)
|
||||
CONVERT_TO_FP16_TEMPLATE(float)
|
||||
CONVERT_TO_FP16_TEMPLATE(double)
|
||||
|
||||
inline void convertBoolToFp16Impl(
|
||||
const bool* __restrict src,
|
||||
at::Half* __restrict dst,
|
||||
int64_t n) {
|
||||
const uint8_t* srcPtr = reinterpret_cast<const uint8_t*>(src);
|
||||
float16_t* dstPtr = reinterpret_cast<float16_t*>(dst);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
dstPtr[i] = srcPtr[i] != 0 ? 1.0 : 0;
|
||||
}
|
||||
}
|
||||
|
||||
template <>
|
||||
inline void convert(const bool* src, at::Half* dst, int64_t n) {
|
||||
return convertBoolToFp16Impl(src, dst, n);
|
||||
}
|
||||
|
||||
inline void convertFp16ToBoolImpl(
|
||||
const at::Half* __restrict src,
|
||||
bool* __restrict dst,
|
||||
int64_t n) {
|
||||
const float16_t* srcPtr = reinterpret_cast<const float16_t*>(src);
|
||||
uint8_t* dstPtr = reinterpret_cast<uint8_t*>(dst);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
dstPtr[i] = srcPtr[i] != 0.0 ? 1 : 0;
|
||||
}
|
||||
}
|
||||
|
||||
template <>
|
||||
inline void convert(const at::Half* src, bool* dst, int64_t n) {
|
||||
return convertFp16ToBoolImpl(src, dst, n);
|
||||
}
|
||||
|
||||
#endif
|
||||
#ifdef __ARM_FEATURE_BF16
|
||||
CONVERT_TEMPLATE(bfloat16_t, uint8_t)
|
||||
@ -207,44 +124,6 @@ CONVERT_TEMPLATE(int32_t, bfloat16_t)
|
||||
CONVERT_TEMPLATE(int64_t, bfloat16_t)
|
||||
CONVERT_TEMPLATE(float, bfloat16_t)
|
||||
CONVERT_TEMPLATE(double, bfloat16_t)
|
||||
|
||||
inline void convertBoolToBfloat16Impl(
|
||||
const bool* __restrict src,
|
||||
c10::BFloat16* __restrict dst,
|
||||
int64_t n) {
|
||||
const uint8_t* srcPtr = reinterpret_cast<const uint8_t*>(src);
|
||||
uint16_t* dstPtr = reinterpret_cast<uint16_t*>(dst);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
constexpr uint16_t kBf16One = 0x3f80; // 1.0 in bfloat16
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
dstPtr[i] = srcPtr[i] != 0 ? kBf16One : 0;
|
||||
}
|
||||
}
|
||||
|
||||
template <>
|
||||
inline void convert(const bool* src, c10::BFloat16* dst, int64_t n) {
|
||||
return convertBoolToBfloat16Impl(src, dst, n);
|
||||
}
|
||||
|
||||
inline void convertBfloat16ToBoolImpl(
|
||||
const c10::BFloat16* __restrict src,
|
||||
bool* __restrict dst,
|
||||
int64_t n) {
|
||||
uint8_t* dstPtr = reinterpret_cast<uint8_t*>(dst);
|
||||
const uint16_t* srcPtr = reinterpret_cast<const uint16_t*>(src);
|
||||
uint64_t len = static_cast<uint64_t>(n);
|
||||
for (uint64_t i = 0; i < len; i++) {
|
||||
// Check if all non-sign bits are 0
|
||||
bool isBf16Zero = (srcPtr[i] & 0x7fff) == 0;
|
||||
dstPtr[i] = isBf16Zero ? 0 : 1;
|
||||
}
|
||||
}
|
||||
|
||||
template <>
|
||||
inline void convert(const c10::BFloat16* src, bool* dst, int64_t n) {
|
||||
return convertBfloat16ToBoolImpl(src, dst, n);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#endif
|
||||
|
||||
@ -309,7 +309,7 @@ class Vectorized<float> {
|
||||
DEFINE_SLEEF_COMPATIBLE_UNARY_ELEMENTWISE_FUNC(expm1)
|
||||
// Implementation copied from Arm Optimized Routine
|
||||
// https://github.com/ARM-software/optimized-routines/blob/master/math/aarch64/advsimd/expf.c
|
||||
inline Vectorized<float> vexpq_f32_u20() const {
|
||||
Vectorized<float> exp_u20() const {
|
||||
// bail out to sleef if it's a special case:
|
||||
// i.e. there's an input s.t. |input| > 87.3....
|
||||
const float32x4_t special_bound = vdupq_n_f32(0x1.5d5e2ap+6f);
|
||||
@ -348,9 +348,6 @@ class Vectorized<float> {
|
||||
|
||||
return vfmaq_f32(scale, poly, scale);
|
||||
}
|
||||
Vectorized<float> exp_u20() const {
|
||||
return vexpq_f32_u20();
|
||||
}
|
||||
Vectorized<float> fexp_u20() const {
|
||||
return exp_u20();
|
||||
}
|
||||
@ -637,7 +634,7 @@ inline Vectorized<float> Vectorized<float>::erf() const {
|
||||
// - exp(- x * x)
|
||||
auto pow_2 = (*this) * (*this);
|
||||
auto neg_pow_2 = pow_2 ^ neg_zero_vec;
|
||||
auto tmp4 = neg_pow_2.vexpq_f32_u20();
|
||||
auto tmp4 = neg_pow_2.exp();
|
||||
auto tmp5 = tmp4 ^ neg_zero_vec;
|
||||
// erf(x) = sign(x) * (1 - r * t * exp(- x * x))
|
||||
auto tmp6 = t * tmp5;
|
||||
|
||||
@ -498,8 +498,8 @@ static inline Vectorized<T> binary_fp8_op_as_fp32(
|
||||
|
||||
// Refer to
|
||||
// https://github.com/pytorch/pytorch/pull/153364#discussion_r2086509353 FP8 +,
|
||||
// -, *, /, planned to be deleted in the future and here is just to make
|
||||
// compiler happy
|
||||
// -, *, /, planed to be deleted in the future and here is just to make compiler
|
||||
// happy
|
||||
Vectorized<Float8_e4m3fn> inline operator+(
|
||||
const Vectorized<Float8_e4m3fn>& a,
|
||||
const Vectorized<Float8_e4m3fn>& b) {
|
||||
@ -585,8 +585,8 @@ class Vectorized<Float8_e5m2> : public Vectorizedf8<Float8_e5m2> {
|
||||
|
||||
// Refer to
|
||||
// https://github.com/pytorch/pytorch/pull/153364#discussion_r2086509353 FP8 +,
|
||||
// -, *, /, planned to be deleted in the future and here is just to make
|
||||
// compiler happy
|
||||
// -, *, /, planed to be deleted in the future and here is just to make compiler
|
||||
// happy
|
||||
Vectorized<Float8_e5m2> inline operator+(
|
||||
const Vectorized<Float8_e5m2>& a,
|
||||
const Vectorized<Float8_e5m2>& b) {
|
||||
|
||||
@ -1,90 +1,78 @@
|
||||
#include <ATen/cuda/CUDAGreenContext.h>
|
||||
|
||||
#if defined(CUDA_VERSION) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
|
||||
#include <c10/cuda/driver_api.h>
|
||||
#include <stdexcept>
|
||||
#include <vector>
|
||||
#define HAS_CUDA_GREEN_CONTEXT() 1
|
||||
#else
|
||||
#define HAS_CUDA_GREEN_CONTEXT() 0
|
||||
// Suppress unused private field warnings as this class is not supposed to be called
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-private-field")
|
||||
#endif
|
||||
|
||||
namespace at::cuda {
|
||||
GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
int driver_version;
|
||||
C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
|
||||
TORCH_CHECK(
|
||||
driver_version >= 12080, "cuda driver too old to use green context!");
|
||||
CUcontext pctx = nullptr;
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
|
||||
if (C10_UNLIKELY(!pctx)) {
|
||||
TORCH_WARN(
|
||||
"Attempted to create a green context but"
|
||||
" there was no primary context! Creating a primary context...");
|
||||
|
||||
GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
int driver_version;
|
||||
C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
|
||||
TORCH_CHECK(
|
||||
driver_version >= 12080, "cuda driver too old to use green context!");
|
||||
CUcontext pctx = nullptr;
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
|
||||
if (C10_UNLIKELY(!pctx)) {
|
||||
TORCH_WARN(
|
||||
"Attempted to create a green context but"
|
||||
" there was no primary context! Creating a primary context...");
|
||||
cudaFree(0);
|
||||
}
|
||||
|
||||
cudaFree(0);
|
||||
}
|
||||
CUdevice device;
|
||||
device_id_ = device_id;
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
|
||||
|
||||
CUdevice device;
|
||||
device_id_ = device_id;
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
|
||||
// Get device resources
|
||||
CUdevResource device_resource;
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
|
||||
device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
|
||||
|
||||
// Get device resources
|
||||
CUdevResource device_resource;
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
|
||||
device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
|
||||
// Split resources
|
||||
std::vector<CUdevResource> result(1);
|
||||
auto result_data = result.data();
|
||||
unsigned int nb_groups = 1;
|
||||
CUdevResource remaining;
|
||||
|
||||
// Split resources
|
||||
std::vector<CUdevResource> result(1);
|
||||
auto result_data = result.data();
|
||||
unsigned int nb_groups = 1;
|
||||
CUdevResource remaining;
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
|
||||
result_data,
|
||||
&nb_groups,
|
||||
&device_resource,
|
||||
&remaining,
|
||||
0, // default flags
|
||||
num_sms));
|
||||
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
|
||||
result_data,
|
||||
&nb_groups,
|
||||
&device_resource,
|
||||
&remaining,
|
||||
0, // default flags
|
||||
num_sms));
|
||||
TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
|
||||
|
||||
TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
|
||||
// Generate resource descriptor
|
||||
CUdevResourceDesc desc;
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
|
||||
&desc, result_data, 1));
|
||||
|
||||
// Generate resource descriptor
|
||||
CUdevResourceDesc desc;
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
|
||||
&desc, result_data, 1));
|
||||
// Create green context
|
||||
// CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
|
||||
// https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
|
||||
&green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
|
||||
|
||||
// Create green context
|
||||
// CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
|
||||
// https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
|
||||
C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
|
||||
&green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
|
||||
|
||||
// Convert to regular context
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
|
||||
TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
|
||||
// Convert to regular context
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
|
||||
TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
|
||||
#else
|
||||
TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
|
||||
TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
|
||||
#endif
|
||||
}
|
||||
|
||||
std::unique_ptr<GreenContext> GreenContext::create(
|
||||
uint32_t num_sms,
|
||||
std::optional<uint32_t> device_id) {
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
if (!device_id.has_value()) {
|
||||
device_id = at::cuda::current_device();
|
||||
}
|
||||
return std::unique_ptr<GreenContext>(new GreenContext(device_id.value(), num_sms));
|
||||
return std::make_unique<GreenContext>(device_id.value(), num_sms);
|
||||
#else
|
||||
TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
|
||||
#endif
|
||||
@ -92,7 +80,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
|
||||
// Implement move operations
|
||||
GreenContext::GreenContext(GreenContext&& other) noexcept{
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
device_id_ = std::exchange(other.device_id_, -1);
|
||||
green_ctx_ = std::exchange(other.green_ctx_, nullptr);
|
||||
context_ = std::exchange(other.context_, nullptr);
|
||||
@ -103,7 +91,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
}
|
||||
|
||||
GreenContext& GreenContext::operator=(GreenContext&& other) noexcept{
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
if (this != &other) {
|
||||
// Clean up current resources
|
||||
if (green_ctx_) {
|
||||
@ -132,7 +120,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
}
|
||||
|
||||
GreenContext::~GreenContext() noexcept{
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
C10_CUDA_DRIVER_CHECK(
|
||||
c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
|
||||
#else
|
||||
@ -140,9 +128,25 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
#endif
|
||||
}
|
||||
|
||||
// Get the underlying CUDA context
|
||||
CUcontext GreenContext::getContext() const {
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
return context_;
|
||||
#else
|
||||
TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
|
||||
#endif
|
||||
}
|
||||
|
||||
// Get the underlying green context
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
CUgreenCtx GreenContext::getGreenContext() const {
|
||||
return green_ctx_;
|
||||
}
|
||||
#endif
|
||||
|
||||
// Make this context current
|
||||
void GreenContext::setContext() {
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
auto current_stream = c10::cuda::getCurrentCUDAStream();
|
||||
parent_stream_ = current_stream.stream();
|
||||
|
||||
@ -171,7 +175,7 @@ GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
|
||||
}
|
||||
|
||||
void GreenContext::popContext() {
|
||||
#if HAS_CUDA_GREEN_CONTEXT()
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
// see above note about stream being hardcoded to the default stream
|
||||
at::cuda::CUDAEvent ev;
|
||||
ev.record(c10::cuda::getCurrentCUDAStream());
|
||||
|
||||
@ -1,38 +1,53 @@
|
||||
#pragma once
|
||||
#include <ATen/cuda/CUDAEvent.h>
|
||||
#include <cuda.h>
|
||||
|
||||
// Forward declare green context as opaque ptr
|
||||
typedef struct CUgreenCtx_st* CUgreenCtx;
|
||||
#if defined(CUDA_VERSION) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
|
||||
#include <c10/cuda/driver_api.h>
|
||||
#include <cuda.h>
|
||||
#include <memory>
|
||||
#include <stdexcept>
|
||||
#include <vector>
|
||||
#define CUDA_HAS_GREEN_CONTEXT 1
|
||||
#else
|
||||
#define CUDA_HAS_GREEN_CONTEXT 0
|
||||
#endif
|
||||
|
||||
namespace at::cuda {
|
||||
|
||||
class TORCH_CUDA_CPP_API GreenContext {
|
||||
public:
|
||||
// Green context creation
|
||||
static std::unique_ptr<GreenContext> create(
|
||||
uint32_t num_sms,
|
||||
std::optional<uint32_t> device_id);
|
||||
~GreenContext() noexcept;
|
||||
GreenContext(uint32_t device_id, uint32_t num_sms);
|
||||
|
||||
static std::unique_ptr<GreenContext> create(uint32_t num_sms, std::optional<uint32_t> device_id);
|
||||
|
||||
// Delete copy constructor and assignment
|
||||
GreenContext(const GreenContext&) = delete;
|
||||
GreenContext& operator=(const GreenContext&) = delete;
|
||||
|
||||
// Implement move operations
|
||||
GreenContext(GreenContext&& other) noexcept;
|
||||
GreenContext& operator=(GreenContext&& other) noexcept;
|
||||
~GreenContext() noexcept;
|
||||
|
||||
// Get the underlying CUDA context
|
||||
CUcontext getContext() const;
|
||||
|
||||
// Get the underlying green context
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
CUgreenCtx getGreenContext() const;
|
||||
#endif
|
||||
|
||||
// Make this context current
|
||||
void setContext();
|
||||
|
||||
void popContext();
|
||||
|
||||
private:
|
||||
GreenContext(uint32_t device_id, uint32_t num_sms);
|
||||
// Implement move operations
|
||||
GreenContext(GreenContext&& other) noexcept;
|
||||
GreenContext& operator=(GreenContext&& other) noexcept;
|
||||
|
||||
#if CUDA_HAS_GREEN_CONTEXT
|
||||
int32_t device_id_ = -1;
|
||||
CUgreenCtx green_ctx_ = nullptr;
|
||||
CUcontext context_ = nullptr;
|
||||
cudaStream_t parent_stream_ = nullptr;
|
||||
#endif
|
||||
};
|
||||
} // namespace at::cuda
|
||||
|
||||
@ -7,6 +7,17 @@
|
||||
#endif
|
||||
|
||||
|
||||
#if defined(USE_ROCM)
|
||||
// hipSparse const API added in v2.4.0
|
||||
#if HIPSPARSE_VERSION >= 200400
|
||||
#define AT_USE_HIPSPARSE_GENERIC_API() 1
|
||||
#else
|
||||
#define AT_USE_HIPSPARSE_GENERIC_API() 1
|
||||
#endif
|
||||
#else // USE_ROCM
|
||||
#define AT_USE_HIPSPARSE_GENERIC_API() 0
|
||||
#endif // USE_ROCM
|
||||
|
||||
// cuSparse Generic API spsv function was added in CUDA 11.3.0
|
||||
#if defined(CUDART_VERSION) && defined(CUSPARSE_VERSION) && (CUSPARSE_VERSION >= 11500)
|
||||
#define AT_USE_CUSPARSE_GENERIC_SPSV() 1
|
||||
|
||||
@ -179,7 +179,7 @@ CuSparseSpMatCsrDescriptor::CuSparseSpMatCsrDescriptor(const Tensor& input, int6
|
||||
batch_offset * values_batch_stride * values.itemsize(),
|
||||
index_type, // data type of row offsets index
|
||||
index_type, // data type of col indices
|
||||
CUSPARSE_INDEX_BASE_ZERO, // base index of row offset and col index
|
||||
CUSPARSE_INDEX_BASE_ZERO, // base index of row offset and col indes
|
||||
value_type // data type of values
|
||||
));
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@ namespace at::cuda {
|
||||
//
|
||||
// A caching allocator for CUDA host allocations (pinned memory).
|
||||
//
|
||||
// This provides a drop-in replacement for THCudaHostAllocator, which reuses
|
||||
// This provides a drop-in replacement for THCudaHostAllocator, which re-uses
|
||||
// freed pinned (page-locked) memory allocations. This avoids device
|
||||
// synchronizations due to cudaFreeHost calls.
|
||||
//
|
||||
@ -26,7 +26,7 @@ inline TORCH_CUDA_CPP_API at::HostAllocator* getCachingHostAllocator() {
|
||||
}
|
||||
|
||||
// Records an event in the specified stream. The allocation corresponding to the
|
||||
// input `ptr`/`ctx` will not be reused until the event has occurred.
|
||||
// input `ptr`/`ctx` will not be re-used until the event has occurred.
|
||||
C10_DEPRECATED_MESSAGE(
|
||||
"at::cuda::CachingHostAllocator_recordEvent(...) is deprecated. Please use at::getHostAllocator(at::kCUDA)->record_event(...) instead.")
|
||||
inline TORCH_CUDA_CPP_API bool CachingHostAllocator_recordEvent(
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
#include <ATen/cuda/CUDAContextLight.h>
|
||||
#include <ATen/cuda/Sleep.h>
|
||||
|
||||
#include <c10/cuda/CUDACachingAllocator.h>
|
||||
#include <c10/cuda/CUDAException.h>
|
||||
#include <c10/cuda/CUDAStream.h>
|
||||
|
||||
@ -25,22 +24,8 @@ __global__ void spin_kernel(int64_t cycles) {
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
thread_local int *flag = nullptr;
|
||||
|
||||
__global__ void busy_wait_for_flag_kernel(int *flag) {
|
||||
atomicExch(flag, 1);
|
||||
while (atomicAdd(flag, 0) == 1) {
|
||||
// do nothing
|
||||
}
|
||||
}
|
||||
|
||||
__global__ void clear_flag_kernel(int *flag) {
|
||||
atomicExch(flag, 0);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
void sleep(int64_t cycles) {
|
||||
dim3 grid(1);
|
||||
dim3 block(1);
|
||||
@ -48,26 +33,6 @@ void sleep(int64_t cycles) {
|
||||
C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
|
||||
void busy_wait_for_flag() {
|
||||
if (!flag) {
|
||||
flag = (int*)c10::cuda::CUDACachingAllocator::raw_alloc(sizeof(int));
|
||||
}
|
||||
dim3 grid(1);
|
||||
dim3 block(1);
|
||||
busy_wait_for_flag_kernel<<<grid, block, 0, c10::cuda::getCurrentCUDAStream()>>>(flag);
|
||||
C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
|
||||
void clear_flag() {
|
||||
if (!flag) {
|
||||
flag = (int*)c10::cuda::CUDACachingAllocator::raw_alloc(sizeof(int));
|
||||
}
|
||||
dim3 grid(1);
|
||||
dim3 block(1);
|
||||
clear_flag_kernel<<<grid, block, 0, c10::cuda::getCurrentCUDAStream()>>>(flag);
|
||||
C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
}
|
||||
|
||||
#ifdef USE_ROCM
|
||||
__global__ void flush_icache_kernel()
|
||||
{
|
||||
|
||||
@ -7,11 +7,6 @@ namespace at::cuda {
|
||||
// enqueues a kernel that spins for the specified number of cycles
|
||||
TORCH_CUDA_CU_API void sleep(int64_t cycles);
|
||||
|
||||
// enqueues a kernel that spins until a flag is cleared by a
|
||||
// corresponding call to clear_flag()
|
||||
TORCH_CUDA_CU_API void busy_wait_for_flag();
|
||||
TORCH_CUDA_CU_API void clear_flag();
|
||||
|
||||
// flushes instruction cache for ROCm; no-op for CUDA
|
||||
TORCH_CUDA_CU_API void flush_icache();
|
||||
|
||||
|
||||
@ -93,7 +93,7 @@ struct IndexToOffset {
|
||||
}
|
||||
};
|
||||
|
||||
// Uses dynamic (runtime) instead of static (compile time) dims
|
||||
// Uses dynamic (runtime) instead of static (compiletime) dims
|
||||
template <typename T, typename IndexType>
|
||||
struct IndexToOffset<T, IndexType, -1> {
|
||||
static inline __host__ __device__ IndexType get(
|
||||
|
||||
@ -32,7 +32,7 @@ static inline void launch_jitted_vectorized_kernel_dynamic(
|
||||
|
||||
// Different kernels are compiled depending on what we're vectorizing up to (1, 2 or 4 elements)
|
||||
// fn_ptr is set to the appropriate function based on the vec size and GPU used
|
||||
// TODO: Memory use can probably be optimized by reusing kernels across GPUs with
|
||||
// TODO: Memory use can probably be optimized by re-using kernels across GPUs with
|
||||
// the same compute capability
|
||||
|
||||
std::string f_inputs_type_str = at::cuda::jit::typeName(common_dtype);
|
||||
|
||||
@ -580,7 +580,7 @@ std::ofstream& TuningContext::GetUntunedFile(){
|
||||
filename.append(device);
|
||||
}
|
||||
|
||||
untuned_file_ = std::ofstream(filename, std::ios::out | std::ios::app);
|
||||
untuned_file_ = std::ofstream(filename, std::ios::out | std::ios::trunc);
|
||||
}
|
||||
return untuned_file_;
|
||||
}
|
||||
|
||||
@ -143,7 +143,7 @@ struct TORCH_API VmapPhysicalView {
|
||||
// mapping a physical tensor to a new logical tensor (BatchedTensor)
|
||||
VmapPhysicalToLogicalMap getPhysicalToLogicalMap() const;
|
||||
|
||||
// Maps a logical shape to a physical shape by prepending the batch
|
||||
// Maps a logical shape to a physical shape by pre-pending the batch
|
||||
// sizes to the logical shape.
|
||||
VmapDimVector getPhysicalShape(IntArrayRef logical_shape) const;
|
||||
SymDimVector getPhysicalShape(c10::SymIntArrayRef logical_shape) const;
|
||||
|
||||
@ -27,7 +27,7 @@ namespace at::functorch {
|
||||
//
|
||||
// There are alternative designs we could have chosen (e.g. each grad transform
|
||||
// stores a weak map of Tensor -> AutogradMeta); the benefit of the TensorWrapper
|
||||
// design is that we can reuse existing VariableType kernels (i.e. Autograd kernels)
|
||||
// design is that we can re-use existing VariableType kernels (i.e. Autograd kernels)
|
||||
// without much modification. Since a TensorWrapper looks like a regular Tensor,
|
||||
// the VariableType kernel can pull out the AutogradMeta struct from where it
|
||||
// expects and extend the autograd graph
|
||||
|
||||
@ -410,8 +410,8 @@ struct ConvParams {
|
||||
return false;
|
||||
}
|
||||
static long cudnn_version = detail::getCUDAHooks().versionCuDNN();
|
||||
// broken on cuDNN 9.8 - 9.14
|
||||
if (cudnn_version >= 90800 && cudnn_version < 91500) {
|
||||
// broken on cuDNN 9.8
|
||||
if (cudnn_version >= 90800) {
|
||||
if (cudnn_conv_suggest_memory_format(input, weight) == at::MemoryFormat::Contiguous &&
|
||||
(input.scalar_type() == at::kBFloat16 || input.scalar_type() == at::kHalf) &&
|
||||
weight.dim() == 5) {
|
||||
@ -689,10 +689,6 @@ static void check_shape_forward(const at::Tensor& input,
|
||||
", but got bias of size ", at::symint::sizes<T>(bias), " instead");
|
||||
|
||||
for (const auto i : c10::irange(2, k)) {
|
||||
// T could be int64_t or SymInt, Specialized numeric_limts<SymInt> in c10/core/SymInt.h
|
||||
TORCH_CHECK(padding[i-2] <= (std::numeric_limits<T>::max() - padding[i-2]),
|
||||
"Given padding=", padding[i-2], " at dimension ", i-2, " , expected padding to be at most ",
|
||||
(std::numeric_limits<T>::max() / 2));
|
||||
input_shape.push_back(at::symint::size<T>(input, i) + 2 * padding[i-2]);
|
||||
// log new kernel size considering dilation
|
||||
kernel_shape.push_back(dilation[i-2] * (weight_sizes[i]-1) + 1);
|
||||
@ -719,11 +715,6 @@ static void check_shape_forward(const at::Tensor& input,
|
||||
"Kernel size: (", kernel_ss.str(), "). Kernel size can't be greater than actual input size");
|
||||
}
|
||||
} else { // transposed
|
||||
for (const auto i : c10::irange(2, k)) {
|
||||
TORCH_CHECK(padding[i-2] <= (std::numeric_limits<T>::max() - padding[i-2]),
|
||||
"Given padding=", padding[i-2], " at dimension ", i-2, " , expected padding to be at most ",
|
||||
(std::numeric_limits<T>::max() / 2));
|
||||
}
|
||||
TORCH_CHECK(at::symint::size<T>(input, 1) == weight_sizes[0],
|
||||
"Given transposed=", transposed, ", weight of size ", weight_sizes,
|
||||
", expected input", at::symint::sizes<T>(input), " to have ", weight_sizes[0],
|
||||
|
||||
@ -52,7 +52,8 @@ Tensor conv_tbc(const Tensor& self, const Tensor& weight, const Tensor& bias, in
|
||||
for (const auto k : c10::irange(kw)) {
|
||||
int iShift = std::max(0, static_cast<int>(k - real_pad));
|
||||
int oShift = std::max(0, static_cast<int>(real_pad - k));
|
||||
long t = std::min(ilen + real_pad - k, olen) - oShift;
|
||||
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||||
int t = std::min(ilen + real_pad - k, olen) - oShift;
|
||||
// Note: gemm assumes column-major matrices
|
||||
// input is l*m (row-major)
|
||||
// weight is m*r (row-major)
|
||||
|
||||
@ -16,7 +16,8 @@ bool canUse32BitIndexMath(const TensorBase& t, int64_t max_elem) {
|
||||
auto linearId = elements - 1;
|
||||
|
||||
// NOTE: Assumes all strides are positive, which is true for now
|
||||
for (auto i = t.dim() - 1; i >= 0; --i) {
|
||||
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||||
for (int i = t.dim() - 1; i >= 0; --i) {
|
||||
auto curDimIndex = linearId % t.sym_size(i);
|
||||
auto curDimOffset = curDimIndex * t.sym_stride(i);
|
||||
offset += curDimOffset;
|
||||
|
||||
@ -68,6 +68,7 @@ Tensor fbgemm_linear_int8_weight_fp32_activation(
|
||||
const float* input_ptr = input_contig.const_data_ptr<float>();
|
||||
|
||||
TORCH_CHECK(input.dim() >= 2);
|
||||
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||||
const int64_t M = size_to_dim_(input.dim() - 1, input.sizes());
|
||||
const int64_t K = input.size(input.dim() - 1);
|
||||
TORCH_CHECK(weight.dim() == 2);
|
||||
|
||||
@ -160,9 +160,10 @@ struct Dist {
|
||||
// value of k.
|
||||
parallel_for(0, combs, internal::GRAIN_SIZE / (16 * m), [p, self_start, self_end, n, m, res_start](int64_t k, int64_t end) {
|
||||
const Vec pvec(p);
|
||||
double n2 = static_cast<double>(n) - .5;
|
||||
double n2 = n - .5;
|
||||
// The -1 accounts for floating point truncation issues
|
||||
int64_t i = static_cast<int64_t>((n2 - std::sqrt(n2 * n2 - 2.0 * static_cast<double>(k) - 1.0)));
|
||||
// NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
|
||||
int64_t i = static_cast<int64_t>((n2 - std::sqrt(n2 * n2 - 2 * k - 1)));
|
||||
int64_t j = k - n * i + i * (i + 1) / 2 + i + 1;
|
||||
|
||||
const scalar_t * self_i = self_start + i * m;
|
||||
|
||||
@ -1017,7 +1017,7 @@ struct HelperInterpBase {
|
||||
while (aligned_interp_size % sizeof(int32_t) != 0) {
|
||||
aligned_interp_size += 1;
|
||||
}
|
||||
// assert that we won't go out of bounds
|
||||
// assert that we wont go out of bounds
|
||||
TORCH_INTERNAL_ASSERT(aligned_interp_size * sizeof(int16_t) < interp_size * sizeof(double));
|
||||
}
|
||||
|
||||
|
||||
@ -655,7 +655,7 @@ void ImagingResampleHorizontalConvolution8u4x(
|
||||
// last element
|
||||
auto mmk = _mm256_set1_epi32(k[i]);
|
||||
// For num_channels == 3 (3 bytes = one pixel) we tolerate to read 4 bytes
|
||||
// lines 0, 1 and 2 won't go out of allocated memory bounds
|
||||
// lines 0, 1 and 2 wont go out of allocated memory bounds
|
||||
auto pix = _mm256_inserti128_si256(_mm256_castsi128_si256(
|
||||
mm_cvtepu8_epi32(lineIn0_min + stride * i, i32_aligned)),
|
||||
mm_cvtepu8_epi32(lineIn1_min + stride * i, i32_aligned), 1);
|
||||
@ -1312,7 +1312,7 @@ void ImagingResampleVerticalConvolution8u(
|
||||
|
||||
// Here we write 4 bytes to the output even if num_channels < 4, e.g o = {r,g,b,X} for num_channels=3
|
||||
// It is OK to write 4th byte (e.g. X) as on the next step we will overwrite it with new data.
|
||||
// We also won't go out of bounds of lineOut memory allocation
|
||||
// We also wont go out of bounds of lineOut memory allocation
|
||||
std::memcpy(lineOut + j, (uint8_t *) &o, 4);
|
||||
}
|
||||
|
||||
|
||||
@ -705,7 +705,7 @@ namespace {
|
||||
);
|
||||
} while (!done && max_threads);
|
||||
if (!done) {
|
||||
TORCH_INTERNAL_ASSERT(false, "Couldn't reduce launch bounds to accommodate sharedMemPerBlock limit");
|
||||
TORCH_INTERNAL_ASSERT(false, "Couldn't reduce launch bounds to accomodate sharedMemPerBlock limit");
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
@ -298,7 +298,7 @@ static void jitted_gpu_kernel_impl(
|
||||
at::opmath_type<f_inputs_type> scalar_val,
|
||||
const std::tuple<ExtraArgs...>& extra_args) {
|
||||
|
||||
// TODO: Memory use can probably be optimized by reusing kernels across GPUs with
|
||||
// TODO: Memory use can probably be optimized by re-using kernels across GPUs with
|
||||
// the same compute capability
|
||||
static std::mutex jiterator_mutex;
|
||||
static std::vector<JittedKernelVariantCache> device_caches(c10::cuda::device_count());
|
||||
|
||||
@ -75,7 +75,7 @@ fused_dropout_kernel_vec(at::cuda::detail::TensorInfo<const scalar_t, IndexType>
|
||||
// We'll use this to actually cause vectorized loads later
|
||||
LoadT *value = reinterpret_cast<LoadT*>(&src);
|
||||
|
||||
//curand_uniform_double was pure evil anyway, not doing what it promises, and there's nothing for Halfs, so generate float for everything
|
||||
//curand_uniform_double was pure evil anyway, not doing what it promises, and there's nothing for halfs, so generate float for everything
|
||||
// Note: need a new set of random values per 4 elements -- we'll handle VEC elements in this thread, so need ceil(VEC / 4)
|
||||
// sets of rand.
|
||||
if ((VEC >= 4) || (gridxvec_loop_state == 0)) {
|
||||
@ -159,7 +159,7 @@ fused_dropout_kernel(cuda::detail::TensorInfo<const scalar_t, IndexType> a,
|
||||
for (IndexType linearIndex = idx;
|
||||
linearIndex < rounded_size;
|
||||
linearIndex += gridDim.x * blockDim.x*UNROLL) {
|
||||
//curand_uniform_double was pure evil anyway, not doing what it promises, and there's nothing for Halfs, so generate float for everything
|
||||
//curand_uniform_double was pure evil anyway, not doing what it promises, and there's nothing for halfs, so generate float for everything
|
||||
float4 rand = curand_uniform4(&state);
|
||||
scalar_t src[UNROLL];
|
||||
rand.x = rand.x < p;
|
||||
|
||||
@ -24,7 +24,7 @@ namespace at::native {
|
||||
namespace {
|
||||
|
||||
/* This code computes the sum of the weights in two-steps:
|
||||
1) Each GPU warp sums `NROWS_PER_THREAD` number of row given by `indices`
|
||||
1) Each GPU warp sums `NROWS_PER_THREAD` number of row given by `indeces`
|
||||
2) Each partial-sum from 1) are summed and scatter into `grad_weight`
|
||||
|
||||
Notice, `NROWS_PER_THREAD` impacts the Achieved Occupancy of the
|
||||
|
||||
@ -204,7 +204,7 @@ Scalar scalar_reciprocal(const Scalar& scalar) {
|
||||
return Scalar(1. / scalar.toComplexDouble());
|
||||
}
|
||||
TORCH_INTERNAL_ASSERT(
|
||||
false, "division with ", scalar.type(), " not supported");
|
||||
false, "divison with ", scalar.type(), " not supported");
|
||||
}
|
||||
|
||||
void foreach_tensor_div_scalar_kernel_cuda_(
|
||||
|
||||
@ -57,7 +57,7 @@ namespace {
|
||||
const index_t n = index / (out_H * out_W);
|
||||
const index_t grid_offset = n * grid_sN + h * grid_sH + w * grid_sW;
|
||||
|
||||
// get the corresponding input x, y coordinates from grid
|
||||
// get the corresponding input x, y co-ordinates from grid
|
||||
opmath_t x = grid.data[grid_offset];
|
||||
opmath_t y = grid.data[grid_offset + grid_sCoor];
|
||||
|
||||
@ -193,7 +193,7 @@ namespace {
|
||||
const index_t n = index / (out_D * out_H * out_W);
|
||||
const index_t grid_offset = n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW;
|
||||
|
||||
// get the corresponding input x, y, z coordinates from grid
|
||||
// get the corresponding input x, y, z co-ordinates from grid
|
||||
opmath_t x = grid.data[grid_offset];
|
||||
opmath_t y = grid.data[grid_offset + grid_sCoor];
|
||||
opmath_t z = grid.data[grid_offset + 2 * grid_sCoor];
|
||||
@ -358,7 +358,7 @@ namespace {
|
||||
const index_t n = index / (out_H * out_W);
|
||||
const auto grid_offset = n * grid_sN + h * grid_sH + w * grid_sW;
|
||||
|
||||
// get the corresponding input x, y coordinates from grid
|
||||
// get the corresponding input x, y co-ordinates from grid
|
||||
scalar_t x = grid.data[grid_offset];
|
||||
scalar_t y = grid.data[grid_offset + grid_sCoor];
|
||||
|
||||
@ -572,7 +572,7 @@ namespace {
|
||||
const index_t n = index / (out_D * out_H * out_W);
|
||||
const auto grid_offset = n * grid_sN + d * grid_sD + h * grid_sH + w * grid_sW;
|
||||
|
||||
// get the corresponding input x, y, z coordinates from grid
|
||||
// get the corresponding input x, y, z co-ordinates from grid
|
||||
scalar_t ix = grid.data[grid_offset];
|
||||
scalar_t iy = grid.data[grid_offset + grid_sCoor];
|
||||
scalar_t iz = grid.data[grid_offset + 2 * grid_sCoor];
|
||||
|
||||
@ -8,7 +8,7 @@
|
||||
#include <c10/util/irange.h>
|
||||
|
||||
|
||||
// Three warnings in Cutlass included header files
|
||||
// Three warninngs in Cutlass included header files
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wset-but-not-used")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-but-set-parameter")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-but-set-variable")
|
||||
|
||||
@ -208,62 +208,6 @@ _f8_f8_bf16_rowwise_grouped_mm(
|
||||
#endif
|
||||
}
|
||||
|
||||
Tensor&
|
||||
_f4_f4_bf16_grouped_mm_fbgemm(
|
||||
const Tensor& mat_a,
|
||||
const Tensor& mat_b,
|
||||
const Tensor& scale_a,
|
||||
const std::optional<Tensor>& global_scale_a,
|
||||
const Tensor& scale_b,
|
||||
const std::optional<Tensor>& global_scale_b,
|
||||
const std::optional<Tensor>& offs,
|
||||
const std::optional<Tensor>& bias,
|
||||
Tensor& out) {
|
||||
#if !defined(USE_ROCM) && defined(USE_FBGEMM_GENAI)
|
||||
// Typing checks
|
||||
TORCH_CHECK_VALUE(mat_a.scalar_type() == at::kFloat4_e2m1fn_x2,
|
||||
"mat_a must be Float4_e2n1fn_2, got: ", mat_a.scalar_type());
|
||||
TORCH_CHECK_VALUE(mat_b.scalar_type() == at::kFloat4_e2m1fn_x2,
|
||||
"mat_b must be Float4_e2n1fn_2, got: ", mat_b.scalar_type());
|
||||
|
||||
std::optional<Tensor> combined_global_scale = std::nullopt;
|
||||
if (global_scale_a.has_value() || global_scale_b.has_value()) {
|
||||
// NVFP4
|
||||
TORCH_CHECK_VALUE(global_scale_a.has_value() && global_scale_b.has_value(),
|
||||
"For NVFP4 grouped gemm both of global_scale_{a,b} must have values")
|
||||
TORCH_CHECK_VALUE(scale_a.scalar_type() == at::kFloat8_e4m3fn,
|
||||
"scale_a must be Float8_e4m3fn, got: ", scale_a.scalar_type());
|
||||
TORCH_CHECK_VALUE(scale_b.scalar_type() == at::kFloat8_e4m3fn,
|
||||
"scale_b must be Float8_e4m3fn, got: ", scale_b.scalar_type());
|
||||
TORCH_CHECK_VALUE(global_scale_a.value().scalar_type() == at::kFloat,
|
||||
"global_scale_a must be Float, got: ", global_scale_a.value().scalar_type());
|
||||
TORCH_CHECK_VALUE(global_scale_b.value().scalar_type() == at::kFloat,
|
||||
"global_scale_b must be Float, got: ", global_scale_b.value().scalar_type());
|
||||
combined_global_scale = global_scale_a.value().mul(global_scale_b.value());
|
||||
} else {
|
||||
// MXFP4
|
||||
TORCH_CHECK_VALUE(scale_a.scalar_type() == at::kFloat8_e8m0fnu,
|
||||
"scale_a must be Float8_e8m0fnu, got: ", scale_a.scalar_type());
|
||||
TORCH_CHECK_VALUE(scale_b.scalar_type() == at::kFloat8_e8m0fnu,
|
||||
"scale_b must be Float8_e8m0fnu, got: ", scale_b.scalar_type());
|
||||
}
|
||||
|
||||
auto o = fbgemm_gpu::f4f4bf16_grouped_mm(
|
||||
mat_a,
|
||||
mat_b,
|
||||
scale_a,
|
||||
scale_b,
|
||||
offs.value(),
|
||||
out,
|
||||
combined_global_scale
|
||||
);
|
||||
#else
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(false, "nvfp4 grouped gemm is not supported without USE_FBGEMM_GENAI, and only for CUDA")
|
||||
#endif
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
void _check_scales_fp8_rowwise(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx, const int scale_multiplier=1) {
|
||||
// Checks scales for 2d or 3d target tensors (`mat`).
|
||||
if (mat.dim() == 2) {
|
||||
@ -301,15 +245,7 @@ void _check_scales_fp8_rowwise(const Tensor& mat, const Tensor& scale, const int
|
||||
}
|
||||
}
|
||||
|
||||
void _check_scales_blocked(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx) {
|
||||
// if {mx,nv}fp4, will need to modify K later
|
||||
bool is_fp4 = (mat.scalar_type() == kFloat4_e2m1fn_x2);
|
||||
int blocksize = 32;
|
||||
// check for nvfp4 vs. mxfp4 to fix blocksize
|
||||
if (is_fp4 && scale.scalar_type() == kFloat8_e4m3fn) {
|
||||
blocksize = 16;
|
||||
}
|
||||
|
||||
void _check_scales_mxfp8(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx) {
|
||||
// Checks scales for 2d or 3d target tensors (`mat`).
|
||||
if (mat.dim() == 2) {
|
||||
// For MXFP8, 2d tensors have variable size groups represented as subtensors,
|
||||
@ -317,19 +253,17 @@ void _check_scales_blocked(const Tensor& mat, const Tensor& scale, const int dim
|
||||
// so we can't check the scale sizes without doing a d2h sync to get the group sizes here.
|
||||
TORCH_CHECK(
|
||||
scale.dim() == mat.dim(),
|
||||
"for block-scaled, scale must have same number of dimensions as parent tensor, but got mat.dim() = ", mat.dim(),
|
||||
" and scale.dim() = ", scale.dim(), " for arg ", arg_idx
|
||||
);
|
||||
"for mxfp8, scale must have same number of dimensions as parent tensor, but got mat.dim() = ", mat.dim(), " and scale.dim() = ", scale.dim(), " for arg ", arg_idx);
|
||||
|
||||
// LHS mat shape (M, total_K) -> scale shape (rounded_up(M, 128), rounded_up_per_group(K/blocksize, 4))
|
||||
// RHS mat shape (total_K, N) -> scale shape (rounded_up(N, 128), rounded_up_per_group(K/blocksize, 4))
|
||||
// LHS mat shape (M, total_K) -> scale shape (rounded_up(M, 128), rounded_up_per_group(K/32, 4))
|
||||
// RHS mat shape (total_K, N) -> scale shape (rounded_up(N, 128), rounded_up_per_group(K/32, 4))
|
||||
// * weight is transposed prior to the call, scale stays non-transposed.
|
||||
bool LHS = arg_idx == 0;
|
||||
int scale_dim_to_check = 0;
|
||||
int mat_dim_to_check = LHS ? 0 : 1;
|
||||
TORCH_CHECK(
|
||||
scale.size(scale_dim_to_check) >= mat.size(mat_dim_to_check),
|
||||
"for block-scaled, arg ", arg_idx, " tensor shape (", mat.size(0), ", ", mat.size(1), ") ",
|
||||
"for mxfp8, arg ", arg_idx, " tensor shape (", mat.size(0), ", ", mat.size(1), ") ",
|
||||
"must have scale.shape[", scale_dim_to_check, "] >= ", mat.size(mat_dim_to_check), " but got scale.shape=(", scale.size(0), ", ", scale.size(1), ")");
|
||||
} else {
|
||||
// For MXFP8, 3d tensors have static group sizes (stack of 2d tensors),
|
||||
@ -339,40 +273,32 @@ void _check_scales_blocked(const Tensor& mat, const Tensor& scale, const int dim
|
||||
};
|
||||
|
||||
// TODO: this is for 3d tensor in 2d-3d case specifically.
|
||||
// We'll need to support 3d-3d and 3d-2d cases once mxfp8/nvfp4 grouped gemm supports them.
|
||||
// We'll need to support 3d-3d and 3d-2d cases once mxfp8 grouped gemm supports them.
|
||||
int64_t G = mat.size(0);
|
||||
int64_t K = mat.size(1);
|
||||
if (is_fp4) {
|
||||
// FP4 packs 2 values into a single 8b word - the "real" K is 2x the
|
||||
// reported K. Reverse that adjustment.
|
||||
const int fp4_elems_per_byte = 2;
|
||||
K *= fp4_elems_per_byte;
|
||||
}
|
||||
int64_t N = mat.size(2);
|
||||
int64_t blocked_scale_K = round_up(K/blocksize, 4);
|
||||
int64_t blocked_scale_K = round_up(K/32, 4);
|
||||
int64_t blocked_scale_N = round_up(N, 128);
|
||||
|
||||
// fbgemm expects stack of flattened blocked scales for 3d tensor, shape (G, blocked_scale_K * blocked_scale_N).
|
||||
TORCH_CHECK(
|
||||
scale.dim() == mat.dim() - 1,
|
||||
"for block-scaled 2d-3d grouped GEMM, the 3d tensor of shape (G,K,N) must have a 2d scale of shape (G, blocked_scale_K * blocked_scale_N),",
|
||||
"but scale is ", scale.dim(), "D for arg ", arg_idx
|
||||
"for mxfp8 2d-3d grouped GEMM, the 3d tensor of shape (G,K,N) must have a 2d scale of shape (G, blocked_scale_K * blocked_scale_N), but scale is ", scale.dim(), "D for arg ", arg_idx
|
||||
);
|
||||
TORCH_CHECK(
|
||||
scale.size(0) == G && scale.size(1) == blocked_scale_K * blocked_scale_N,
|
||||
"for block-scaled grouped GEMM, the tensor shape (", G, ", ", K, ", ", N, ") must have scale shape (", G, ",", blocked_scale_K, ",", blocked_scale_N, ")",
|
||||
" for arg ", arg_idx, ", got: ", scale.size(0), ", ", scale.size(1)
|
||||
"for mxfp8, the tensor shape (", G, ", ", K, ", ", N, ") must have scale shape (", G, ",", blocked_scale_K, ",", blocked_scale_N, ") for arg ", arg_idx
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
void check_scale(const Tensor& mat, const Tensor& scale, const int dim, const int arg_idx, const int scale_multiplier=1) {
|
||||
bool using_fp8_rowwise = scale.scalar_type() == kFloat;
|
||||
bool using_mx = scale.scalar_type() == at::kFloat8_e8m0fnu;
|
||||
bool using_mxfp8 = scale.scalar_type() == at::kFloat8_e8m0fnu;
|
||||
if (using_fp8_rowwise) {
|
||||
_check_scales_fp8_rowwise(mat, scale, dim, arg_idx, scale_multiplier);
|
||||
} else if (using_mx) {
|
||||
_check_scales_blocked(mat, scale, dim, arg_idx);
|
||||
} else if (using_mxfp8) {
|
||||
_check_scales_mxfp8(mat, scale, dim, arg_idx);
|
||||
} else {
|
||||
TORCH_CHECK(false, "scale must be float32 or float8_e8m0fnu, but got ", scale.dtype());
|
||||
}
|
||||
@ -485,11 +411,9 @@ namespace {
|
||||
|
||||
using acceptance_fn = std::function<bool(c10::ScalarType, std::vector<ScalingType>&, ArrayRef<Tensor>&, c10::ScalarType, std::vector<ScalingType>&, ArrayRef<Tensor>&)>;
|
||||
|
||||
std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 4> scale_grouped_kernel_dispatch = {{
|
||||
std::array<std::tuple<std::string, acceptance_fn, ScaledGemmImplementation>, 2> scale_grouped_kernel_dispatch = {{
|
||||
{ "rowwise_rowwise", scaled_blas::check_rowwise_recipe, ScaledGemmImplementation::ROWWISE_ROWWISE},
|
||||
{ "mxfp8_mxfp8", scaled_blas::check_mxfp8_recipe, ScaledGemmImplementation::MXFP8_MXFP8},
|
||||
{ "mxfp4_mxfp4", scaled_blas::check_mxfp4_recipe, ScaledGemmImplementation::MXFP4_MXFP4},
|
||||
{ "nvfp4_nvfp4", scaled_blas::check_nvfp4_recipe, ScaledGemmImplementation::NVFP4_NVFP4}}};
|
||||
{ "mxfp8_mxfp8", scaled_blas::check_mxfp8_recipe, ScaledGemmImplementation::MXFP8_MXFP8}}};
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
@ -525,7 +449,7 @@ _scaled_grouped_mm_cuda_v2(
|
||||
"Contraction dimensions (", dim_a, ",", dim_b, ") of mat_a and mat_b must match, got: ", mat_a.size(dim_a), " and ",
|
||||
mat_b.size(dim_b));
|
||||
// Note: only (-1, -2) is currently supported
|
||||
TORCH_CHECK_VALUE(dim_a == -1 && dim_b == -2, "Currently contraction dims must be (-1, -2) only");
|
||||
TORCH_CHECK_VALUE(dim_a == -1 && dim_b == -2, "Curently contraction dims must be (-1, -2) only");
|
||||
} else {
|
||||
TORCH_CHECK_VALUE(mat_a.size(-1) == mat_b.size(-2), "contraction dimension of mat_a and mat_b must match");
|
||||
}
|
||||
@ -601,9 +525,8 @@ _scaled_grouped_mm_cuda_v2(
|
||||
out);
|
||||
}
|
||||
case ScaledGemmImplementation::MXFP8_MXFP8: {
|
||||
// scale shape checks
|
||||
_check_scales_blocked(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
|
||||
_check_scales_blocked(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
|
||||
_check_scales_mxfp8(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
|
||||
_check_scales_mxfp8(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
|
||||
return _mx8_mx8_bf16_grouped_mm_fbgemm(
|
||||
mat_a,
|
||||
mat_b,
|
||||
@ -614,36 +537,6 @@ _scaled_grouped_mm_cuda_v2(
|
||||
offs.value(),
|
||||
out);
|
||||
}
|
||||
case ScaledGemmImplementation::MXFP4_MXFP4: {
|
||||
// scale shape checks
|
||||
_check_scales_blocked(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
|
||||
_check_scales_blocked(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
|
||||
return _f4_f4_bf16_grouped_mm_fbgemm(
|
||||
mat_a,
|
||||
mat_b,
|
||||
scale_a[0], /* block-scale A */
|
||||
std::nullopt, /* global-scale A */
|
||||
scale_b[0], /* block-scale B */
|
||||
std::nullopt, /* global-scale B */
|
||||
offs.value(),
|
||||
std::nullopt, /* bias */
|
||||
out);
|
||||
}
|
||||
case ScaledGemmImplementation::NVFP4_NVFP4: {
|
||||
// scale shape checks
|
||||
_check_scales_blocked(mat_a, scale_a[0], 0 /* dim */, 0 /* arg_idx */);
|
||||
_check_scales_blocked(mat_b, scale_b[0], 1 /* dim */, 1 /* arg_idx */);
|
||||
return _f4_f4_bf16_grouped_mm_fbgemm(
|
||||
mat_a,
|
||||
mat_b,
|
||||
scale_a[0], /* block-scale A */
|
||||
scale_a[1], /* global-scale A */
|
||||
scale_b[0], /* block-scale B */
|
||||
scale_b[1], /* global-scale B */
|
||||
offs.value(),
|
||||
std::nullopt, /* bias */
|
||||
out);
|
||||
}
|
||||
default:
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(false,
|
||||
"_scaled_grouped_mm_cuda_v2 is in an inconsistent state - should never reach here");
|
||||
|
||||
@ -377,7 +377,7 @@ __noinline__ __host__ __device__ scalar_t calc_igammac(scalar_t a, scalar_t x) {
|
||||
* result at the boundary
|
||||
* - if a is large and a ~ x, then using Uniform Asymptotic Expansions for
|
||||
* Large Parameter (see DLMF 8.12.4 [igam1])
|
||||
* - if x > 1.1 and x < a, using the subtraction from the regularized lower
|
||||
* - if x > 1.1 and x < a, using the substraction from the regularized lower
|
||||
* incomplete gamma
|
||||
* - otherwise, calculate the series from [igam2] eq (5)
|
||||
*/
|
||||
@ -460,7 +460,7 @@ __noinline__ __host__ __device__ scalar_t calc_igamma(scalar_t a, scalar_t x) {
|
||||
* result at the boundary
|
||||
* - if a is large and a ~ x, then using Uniform Asymptotic Expansions for
|
||||
* Large Parameter (see DLMF 8.12.3 [igam1])
|
||||
* - if x > 1 and x > a, using the subtraction from the regularized upper
|
||||
* - if x > 1 and x > a, using the substraction from the regularized upper
|
||||
* incomplete gamma
|
||||
* - otherwise, calculate the series from [igam2] eq (4)
|
||||
*/
|
||||
|
||||
@ -332,7 +332,7 @@ void cuda_take_put_kernel(
|
||||
const auto offset_calc = make_offset_calculator<2>(iter);
|
||||
using uindex_t = std::make_unsigned_t<index_t>;
|
||||
|
||||
// OffsetCalculator needs the sizes and strides reversed
|
||||
// OffsetCalculator needs the sizes and strides reveresed
|
||||
const auto indexed_sizes = std::vector<int64_t>(indexed.sizes().rbegin(), indexed.sizes().rend());
|
||||
const auto indexed_strides = std::vector<int64_t>(indexed.strides().rbegin(), indexed.strides().rend());
|
||||
const auto* indexed_strides_data = indexed_strides.data();
|
||||
|
||||
@ -13,7 +13,7 @@ __global__ void vectorized_gather_kernel(char * out, char * inp, index_t * idx,
|
||||
if (allow_neg_indices) {
|
||||
ind = (ind < 0) ? ind + ind_dim_size : ind;
|
||||
}
|
||||
CUDA_KERNEL_ASSERT_VERBOSE(ind >=0 && ind < ind_dim_size && "vectorized gather kernel index out of bounds", "Expected 0 <= index < ind_dim_size(%ld), but got index = %ld", ind_dim_size, ind);
|
||||
CUDA_KERNEL_ASSERT(ind >=0 && ind < ind_dim_size && "vectorized gather kernel index out of bounds");
|
||||
int32_t off = (blockDim.x * blockIdx.y + threadIdx.x) * Alignment; // off is guaranteed to be within int32 limits
|
||||
if (off >= slice_size) return;
|
||||
auto vec = at::native::memory::ld_vec<Alignment>(inp + ind * inp_stride + off);
|
||||
|
||||
@ -1611,7 +1611,7 @@ void index_select_out_cuda_impl(
|
||||
|
||||
// SmallIndexKernel is more performant when the number of indices is small, and pre-loading
|
||||
// the index reduces memory accesses. When the number of indices is large, we avoid that
|
||||
// and increase parallelism by calling gather_out which is a generalization of index_select
|
||||
// and increase parallellism by calling gather_out which is a generalization of index_select
|
||||
if (cuda::detail::canUse32BitIndexMath(out) &&
|
||||
cuda::detail::canUse32BitIndexMath(self) &&
|
||||
cuda::detail::canUse32BitIndexMath(index) &&
|
||||
|
||||
@ -269,7 +269,7 @@ __device__ __forceinline__ void opportunistic_fastAtomicAdd(
|
||||
|
||||
scalar_t* dst = self_ptr + index;
|
||||
|
||||
//pack coalesced bf16 and fp16
|
||||
//pack coalseced bf16 and fp16
|
||||
if constexpr (std::is_same<scalar_t, c10::BFloat16>::value || std::is_same<scalar_t, c10::Half>::value)
|
||||
{
|
||||
typedef unsigned short __attribute__((ext_vector_type(2))) vec_short2;
|
||||
@ -312,7 +312,7 @@ __device__ __forceinline__ void opportunistic_fastAtomicAdd(
|
||||
}
|
||||
}
|
||||
|
||||
// not coalesced, so now let try to capture lane-matches...
|
||||
// not coalsced, so now let try to capture lane-matches...
|
||||
|
||||
if (numel > 16 /*<-hueristic threshold*/ * 64 ) {
|
||||
// well shucks, unlikely to capture same-dest atomics in a wave.
|
||||
|
||||
@ -343,7 +343,7 @@ ctc_loss_backward_log_beta_gpu_kernel(scalar_t* __restrict__ log_beta_data,
|
||||
if (input_length == 0)
|
||||
return;
|
||||
|
||||
// "first" row, the beta initialization before eq (10) (t=target_length - differs per batch)
|
||||
// "first" row, the beta initialization before eq (10) (t=target_length - differes per batch)
|
||||
for (int64_t block_s = 2*max_target_length - (2*max_target_length % blockDim.x); block_s >= 0; block_s -= blockDim.x) {
|
||||
int64_t s = threadIdx.x + block_s;
|
||||
scalar_t lb;
|
||||
|
||||
@ -816,7 +816,7 @@ const auto erfcx_string = jiterator_stringify(
|
||||
with the usual checks for overflow etcetera.
|
||||
|
||||
Performance-wise, it seems to be substantially faster than either
|
||||
the SLATEC DERFC function [or an erfcx function derived there from]
|
||||
the SLATEC DERFC function [or an erfcx function derived therefrom]
|
||||
or Cody's CALERF function (from netlib.org/specfun), while
|
||||
retaining near machine precision in accuracy.
|
||||
*/
|
||||
|
||||
@ -370,7 +370,7 @@ struct vectorized {
|
||||
|
||||
#ifdef USE_ROCM
|
||||
// This is similar to vectorized policy above, but this one supports
|
||||
// heterogeneous input tensor types as templated parameters.
|
||||
// heterogenous input tensor types as templated parameters.
|
||||
// Its use should be limited to frequently used heterogeneous data types
|
||||
// as each instantiation will generate a separate kernel, leading to code
|
||||
// bloating if applied to all combinations supported in PyTorch. Assumption: all
|
||||
|
||||
@ -309,7 +309,7 @@ __global__ void sampleMultinomialOnce(
|
||||
} else {
|
||||
// This should address a rare bug where we don't select a valid index. This likely occurs when
|
||||
// due to floating point arithmetic rounding errors, our cumulative sum does not add up to 1, but
|
||||
// and our uniform sample is greater than this value. In this case we likely have uninitialized memory
|
||||
// and our uniform sample is greater than this value. In this case we likely have unitialized memory
|
||||
// in dest[curDist]. So basically we will loop through the distribution and pick the largest index
|
||||
// where the distribution is non-zero. This is obviously terribly inefficient, but due to the
|
||||
// rarity in which this occurs, this should not be an issue.
|
||||
|
||||
@ -1654,7 +1654,7 @@ at::Tensor batch_norm_backward_elemt_channels_last_cuda_template(
|
||||
const auto stride = input.sizes()[1];
|
||||
const auto reduction_size = input.numel() / stride;
|
||||
|
||||
// Input is guaranteed to be channels-last compatible
|
||||
// Input is guarunteed to be channels-last compatible
|
||||
at::Tensor grad_input = at::empty_like(input);
|
||||
|
||||
dim3 block;
|
||||
@ -1722,7 +1722,7 @@ at::Tensor batch_norm_backward_elemt_channels_last_cuda_template(
|
||||
const auto reduction_size = input.numel() / stride;
|
||||
auto norm_fct = 1.0 / reduction_size;
|
||||
|
||||
// Input is guaranteed to be channels-last compatible
|
||||
// Input is guarunteed to be channels-last compatible
|
||||
at::Tensor grad_input = at::empty_like(input);
|
||||
|
||||
dim3 block;
|
||||
|
||||
@ -37,7 +37,7 @@ namespace at::native {
|
||||
// threshold probability for having non-duplicate keys, then it can be proved that[1]
|
||||
// the number of bits required is: ceil(log2(n - (6 n^2 + 1) / (12 log(q))))
|
||||
//
|
||||
// Then after sort, we launch a separate kernel that additionally shuffles any islands
|
||||
// Then after sort, we lauch a separate kernel that additionally shuffles any islands
|
||||
// of values whose keys matched. The algorithm of this kernel is as follows:
|
||||
// Each thread reads its key and the keys of its neighbors to tell if it's part of an island.
|
||||
// For each island, the first thread in the island sees a key match at index i+1 but not index i-1.
|
||||
|
||||
@ -1086,12 +1086,12 @@ ReduceConfig setReduceConfig(const TensorIterator& iter){
|
||||
// load instructions.
|
||||
//
|
||||
// Case 1: "vectorize along input"
|
||||
// This case happens when we are reducing along fastest moving dimension. In such case, threads
|
||||
// This case happens when we are reducing along fastest moving dimesion. In such case, threads
|
||||
// with the same threadIdx.y works on the same reduction cooperatively and will produce results
|
||||
// for the same output. In such case, values in each loaded vector always correspond to the same output.
|
||||
//
|
||||
// Case 2: "vectorize along output"
|
||||
// This case happens when the fastest moving dimension is not the dimension of reduction. In such case,
|
||||
// This case happens when the fastest moving dimesion is not the dimension of reduction. In such case,
|
||||
// threads with different threadIdx.x are independent and will produce results for different outputs.
|
||||
// In such case, values in each loaded vector always correspond to different outputs.
|
||||
if (fastest_moving_stride == sizeof(scalar_t)) {
|
||||
|
||||
@ -273,7 +273,7 @@ __global__ void reflection_pad2d_backward_det_out_kernel(
|
||||
const int64_t dist_cols = ::abs(inp_col - (input_dim_x - 1));
|
||||
|
||||
// we were dist_rows after, now we want to be dist_rows before
|
||||
// we were dist_cols before, now we want to be dist_cols after
|
||||
// we were dist_cols before, now we wnat to be dist_cols after
|
||||
const int64_t reflect_tr_out_row = (corner_tr_out_row - dist_rows);
|
||||
const int64_t reflect_tr_out_col = (corner_tr_out_col + dist_cols);
|
||||
const int64_t reflect_tr_out =
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
#include <ATen/cuda/nvrtc_stub/ATenNVRTC.h>
|
||||
#include <c10/macros/Macros.h>
|
||||
|
||||
// Two warnings in Cutlass included header files
|
||||
// Two warninngs in Cutlass included header files
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wset-but-not-used")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-but-set-parameter")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wmissing-field-initializers")
|
||||
|
||||
@ -794,24 +794,6 @@ void _check_deepseek_scale_stride(const Tensor& scale, const Tensor& t, const Sc
|
||||
}
|
||||
}
|
||||
|
||||
void
|
||||
_check_deepseek_support() {
|
||||
#ifndef USE_ROCM
|
||||
auto dprops = at::cuda::getCurrentDeviceProperties();
|
||||
if (dprops->major != 9) {
|
||||
// Only on Hopper GPUs
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
dprops->major == 9,
|
||||
"DeepSeek style (1x128, 128x128) scaling only supported in CUDA for SM90")
|
||||
}
|
||||
// Only in cublasLt >= 12.9
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
CUBLAS_VERSION < 120900 || cublasLtGetVersion() < 120900,
|
||||
"DeepSeek style (1x128, 128x128) scaling requires cublasLt >= 12.9"
|
||||
);
|
||||
#endif
|
||||
}
|
||||
|
||||
Tensor&
|
||||
_scaled_block1x128_block1x128(
|
||||
const Tensor& mat_a, const Tensor& mat_b,
|
||||
@ -820,12 +802,8 @@ _scaled_block1x128_block1x128(
|
||||
const c10::ScalarType out_dtype,
|
||||
const bool use_fast_accum,
|
||||
Tensor& out) {
|
||||
#ifndef USE_ROCM
|
||||
// Restrictions:
|
||||
// A, B are FP8, scales are fp32, shape K//128
|
||||
// CUDA: Only Hopper GPUs
|
||||
_check_deepseek_support();
|
||||
|
||||
TORCH_CHECK_VALUE(isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()), "mat_a and mat_b must be fp8 types, got: ",
|
||||
mat_a.scalar_type(), mat_b.scalar_type());
|
||||
TORCH_CHECK_VALUE(scale_a.sizes()[0] == mat_a.sizes()[0] && scale_a.sizes()[1] == mat_a.sizes()[1] / 128 && scale_a.scalar_type() == kFloat,
|
||||
@ -843,12 +821,6 @@ _scaled_block1x128_block1x128(
|
||||
_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
|
||||
|
||||
return out;
|
||||
#else
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
false,
|
||||
"1x128 and 128x128 scaling not available with ROCm"
|
||||
);
|
||||
#endif
|
||||
}
|
||||
|
||||
Tensor&
|
||||
@ -859,12 +831,10 @@ _scaled_block128x128_block1x128(
|
||||
const c10::ScalarType out_dtype,
|
||||
const bool use_fast_accum,
|
||||
Tensor& out) {
|
||||
#ifndef USE_ROCM
|
||||
// Restrictions:
|
||||
// A, B are FP8, scales are fp32, shape K//128
|
||||
// CUDA: Only Hopper GPUs
|
||||
_check_deepseek_support();
|
||||
|
||||
std::cout << "mat_b: " << mat_b.dim() << ", " << mat_b.sizes() << ", " << mat_b.strides() << std::endl;
|
||||
std::cout << "scale_b: " << scale_b.dim() << ", " << scale_b.sizes() << ", " << scale_b.strides() << std::endl;
|
||||
TORCH_CHECK_VALUE(isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()), "mat_a and mat_b must be fp8 types, got: ",
|
||||
mat_a.scalar_type(), mat_b.scalar_type());
|
||||
TORCH_CHECK_VALUE(scale_a.sizes()[0] == ceil_div<int64_t>(mat_a.sizes()[0], 128) && scale_a.sizes()[1] == ceil_div<int64_t>(mat_a.sizes()[1], 128) && scale_a.scalar_type() == kFloat,
|
||||
@ -882,12 +852,6 @@ _scaled_block128x128_block1x128(
|
||||
_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
|
||||
|
||||
return out;
|
||||
#else
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
false,
|
||||
"1x128 and 128x128 scaling not available with ROCm"
|
||||
);
|
||||
#endif
|
||||
}
|
||||
|
||||
Tensor&
|
||||
@ -898,12 +862,8 @@ _scaled_block1x128_block128x128(
|
||||
const c10::ScalarType out_dtype,
|
||||
const bool use_fast_accum,
|
||||
Tensor& out) {
|
||||
#ifndef USE_ROCM
|
||||
// Restrictions:
|
||||
// A, B are FP8, scales are fp32, A: shape K//128, B: K//128, N//128
|
||||
// CUDA: Only Hopper GPUs
|
||||
_check_deepseek_support();
|
||||
|
||||
TORCH_CHECK_VALUE(isFloat8Type(mat_a.scalar_type()) && isFloat8Type(mat_b.scalar_type()), "mat_a and mat_b must be fp8 types, got: ",
|
||||
mat_a.scalar_type(), mat_b.scalar_type());
|
||||
TORCH_CHECK_VALUE(scale_a.sizes()[0] == mat_a.sizes()[0] && scale_a.sizes()[1] == mat_a.sizes()[1] / 128 && scale_a.scalar_type() == kFloat,
|
||||
@ -921,12 +881,6 @@ _scaled_block1x128_block128x128(
|
||||
_scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, use_fast_accum, out);
|
||||
|
||||
return out;
|
||||
#else
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
false,
|
||||
"1x128 and 128x128 scaling not available with ROCm"
|
||||
);
|
||||
#endif
|
||||
}
|
||||
|
||||
Tensor&
|
||||
|
||||
@ -7,7 +7,7 @@
|
||||
#include <c10/macros/Macros.h>
|
||||
#include <c10/util/irange.h>
|
||||
|
||||
// Two warnings in Cutlass included header files
|
||||
// Two warninngs in Cutlass included header files
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wset-but-not-used")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-but-set-parameter")
|
||||
C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-but-set-variable")
|
||||
|
||||
@ -160,8 +160,8 @@ struct _cuda_scatter_gather_internal_kernel {
|
||||
auto offsets = offset_calc.get(i);
|
||||
|
||||
int64_t idx_dim = *(index_t*)(index_ptr + offsets[2]);
|
||||
CUDA_KERNEL_ASSERT_VERBOSE(idx_dim >= 0 && idx_dim < index_size
|
||||
&& "scatter gather kernel index out of bounds", "Expected 0 <= idx_dim < index_size (%ld), but got idx_dim = %ld", index_size, idx_dim);
|
||||
CUDA_KERNEL_ASSERT(idx_dim >= 0 && idx_dim < index_size
|
||||
&& "scatter gather kernel index out of bounds");
|
||||
|
||||
f(
|
||||
(scalar_t*)(self_ptr + offsets[0]),
|
||||
@ -406,8 +406,9 @@ struct _cuda_scatter_fill_internal_kernel {
|
||||
auto offsets = offset_calc.get(i);
|
||||
|
||||
int64_t idx_dim = *(index_t*)(index_ptr + offsets[1]);
|
||||
CUDA_KERNEL_ASSERT_VERBOSE(idx_dim >= 0 && idx_dim < index_size
|
||||
&& "index out of bounds", "Expected 0 <= idx_dim < index_size (%ld), but got idx_dim = %ld", index_size, idx_dim);
|
||||
CUDA_KERNEL_ASSERT(idx_dim >= 0 && idx_dim < index_size
|
||||
&& "index out of bounds"
|
||||
);
|
||||
|
||||
f(
|
||||
(scalar_t*)(self_ptr + offsets[0]),
|
||||
|
||||
@ -12,15 +12,14 @@
|
||||
|
||||
namespace at::native {
|
||||
|
||||
#if 0 && AT_USE_JITERATOR()
|
||||
#if AT_USE_JITERATOR()
|
||||
constexpr char tan_name[] = "tan_impl";
|
||||
#endif
|
||||
|
||||
void tan_kernel_cuda(TensorIteratorBase& iter) {
|
||||
auto common_dtype = iter.common_dtype();
|
||||
if (at::isComplexType(common_dtype)) {
|
||||
// Disabled due to accuracy issues
|
||||
#if 0 && AT_USE_JITERATOR()
|
||||
#if AT_USE_JITERATOR()
|
||||
static const auto tan_string = jiterator_stringify(
|
||||
template <typename T> T tan_impl(T a) { return std::tan(a); });
|
||||
AT_DISPATCH_COMPLEX_TYPES_AND(
|
||||
|
||||
@ -12,15 +12,14 @@
|
||||
|
||||
namespace at::native {
|
||||
|
||||
#if 0 && AT_USE_JITERATOR()
|
||||
#if AT_USE_JITERATOR()
|
||||
constexpr char tanh_name[] = "tanh_impl";
|
||||
#endif
|
||||
|
||||
void tanh_kernel_cuda(TensorIteratorBase& iter) {
|
||||
auto common_dtype = iter.common_dtype();
|
||||
if (at::isComplexType(common_dtype)) {
|
||||
// Disabled due to accuracy issues
|
||||
#if 0 && AT_USE_JITERATOR()
|
||||
#if AT_USE_JITERATOR()
|
||||
static const auto tanh_string = jiterator_stringify(
|
||||
template <typename T> T tanh_impl(T a) { return std::tanh(a); });
|
||||
AT_DISPATCH_COMPLEX_TYPES_AND(
|
||||
|
||||
@ -460,7 +460,7 @@ __global__ void GammaBetaBackwardCUDAKernel2(
|
||||
}
|
||||
}
|
||||
|
||||
// Do warp reduce for the 2nd 16 cols in the tile.
|
||||
// Do warp reduce for the 2st 16 cols in the tile.
|
||||
sum1 = g_shared[threadIdx.x][threadIdx.y + blockDim.y];
|
||||
sum2 = b_shared[threadIdx.x][threadIdx.y + blockDim.y];
|
||||
sum1 = cuda_utils::WarpReduceSum<T_ACC>(sum1);
|
||||
|
||||
@ -1556,19 +1556,19 @@ NvrtcFunction jit_pwise_function(
|
||||
ss << "_" << hash_code;
|
||||
file_path = ss.str();
|
||||
|
||||
std::ifstream read_stream{file_path, std::ios::in | std::ifstream::binary};
|
||||
if (read_stream.fail()) {
|
||||
std::ifstream readin{file_path, std::ios::in | std::ifstream::binary};
|
||||
if (readin.fail()) {
|
||||
// NOTE: this does not warn because the file might not exist
|
||||
// TODO: consider if this should explicitly check for the file's existence or not to throw
|
||||
// an informative warning
|
||||
read_stream.close();
|
||||
readin.close();
|
||||
} else {
|
||||
// TODO: try passing the "mapped" file directly to cuModuleLoadCall instead of using an intermediate buffer
|
||||
std::vector<char> buffer(std::istreambuf_iterator<char>(read_stream), {});
|
||||
std::vector<char> buffer(std::istreambuf_iterator<char>(readin), {});
|
||||
AT_CUDA_DRIVER_CHECK(nvrtc.cuModuleLoadData(&(compiled_kernel_.module), buffer.data()));
|
||||
AT_CUDA_DRIVER_CHECK(
|
||||
nvrtc.cuModuleGetFunction(&(compiled_kernel_.function), compiled_kernel_.module, name.c_str()));
|
||||
read_stream.close();
|
||||
readin.close();
|
||||
return compiled_kernel_;
|
||||
}
|
||||
}
|
||||
|
||||
@ -141,8 +141,7 @@ WelfordDataLN cuWelfordOnlineSum(
|
||||
if constexpr (!rms_norm){
|
||||
U delta = val - curr_sum.mean;
|
||||
U new_count = curr_sum.count + 1.f;
|
||||
//Due to low CU count, we run into accuracy issues on gfx90a with `__builtin_amdgcn_rcpf`
|
||||
#if defined(USE_ROCM) && !defined(__gfx90a__) && defined(USE_LAYERNORM_FAST_RECIPROCAL)
|
||||
#if defined(USE_ROCM) && defined(USE_LAYERNORM_FAST_RECIPROCAL)
|
||||
U new_mean = curr_sum.mean + delta * __builtin_amdgcn_rcpf(new_count);
|
||||
#else
|
||||
U new_mean = curr_sum.mean + delta * (1.f/new_count); //proper division is slow, this is less accurate but noticeably faster
|
||||
@ -164,8 +163,7 @@ WelfordDataLN cuWelfordCombine(
|
||||
U count = dataA.count + dataB.count;
|
||||
U mean, sigma2;
|
||||
if (count > decltype(dataB.count){0}) {
|
||||
//Due to low CU count, we run into accuracy issues on gfx90a with `__builtin_amdgcn_rcpf`
|
||||
#if defined(USE_ROCM) && !defined(__gfx90a__) && defined(USE_LAYERNORM_FAST_RECIPROCAL)
|
||||
#if defined(USE_ROCM) && defined(USE_LAYERNORM_FAST_RECIPROCAL)
|
||||
auto coef = __builtin_amdgcn_rcpf(count);
|
||||
#else
|
||||
auto coef = 1.f/count; //NB we don't use --use_fast_math, but this is emulation, 1./count goes to intrinsic, `* coef` is multiplication, instead of slow fp division
|
||||
@ -1052,7 +1050,7 @@ void launch_vectorized_layer_norm_kernel(
|
||||
C10_CUDA_KERNEL_LAUNCH_CHECK();
|
||||
|
||||
#ifdef USE_ROCM
|
||||
// the blocks.x contains the max grid x dimension without invalid configuration error
|
||||
// the blocks.x contains the max grid x dimention without invalid configuration error
|
||||
// Fix invalid configuration https://github.com/pytorch/pytorch/issues/136291
|
||||
// Ensure all elements are processed. Prepare for next round
|
||||
int64_t remaining = M - blocks.x;
|
||||
|
||||
@ -177,7 +177,7 @@ bool use_ragged_in_dense(
|
||||
TORCH_WARN_ONCE(
|
||||
"TORCH_CUDNN_SDPA_AVOID_RECOMPILE=1 only works with Q, K, V, and output in BSHD memory layout,"
|
||||
"e.g., Q, K, V must be allocated with torch.randn((B, S, H, D).transpose(1, 2)."
|
||||
"Falling back to regular dense case, which may trigger excessive recompilation.");
|
||||
"Falling back to regualr dense case, which may trigger excessive recompilation.");
|
||||
}
|
||||
return all_bshd;
|
||||
}
|
||||
@ -771,7 +771,7 @@ std::unique_ptr<fe::graph::Graph> build_graph_nestedtensor(
|
||||
if (attn_bias.has_value()) {
|
||||
TORCH_CHECK(
|
||||
false,
|
||||
"attn_bias not yet supported with cuDNN Attention and NestedTensor");
|
||||
"attn_bias not yet supportd with cuDNN Attention and NestedTensor");
|
||||
scaled_dot_product_flash_attention_options.set_bias(
|
||||
mha_graph->tensor(fe::graph::Tensor_attributes()
|
||||
.set_uid(BIAS)
|
||||
@ -1196,7 +1196,7 @@ std::unique_ptr<fe::graph::Graph> build_graph_backward_nestedtensor(
|
||||
if (attn_bias.has_value()) {
|
||||
TORCH_CHECK(
|
||||
false,
|
||||
"attn_bias not yet supported with cuDNN Attention and NestedTensor");
|
||||
"attn_bias not yet supportd with cuDNN Attention and NestedTensor");
|
||||
sdpa_backward_options.set_bias(
|
||||
mha_graph->tensor(fe::graph::Tensor_attributes()
|
||||
.set_uid(BIAS)
|
||||
@ -1864,7 +1864,7 @@ void run_cudnn_SDP_bprop_nestedtensor(
|
||||
}
|
||||
TORCH_CHECK(
|
||||
!attn_bias.has_value(),
|
||||
"attn_bias not yet supported with cuDNN Attention and NestedTensor");
|
||||
"attn_bias not yet supportd with cuDNN Attention and NestedTensor");
|
||||
|
||||
auto workspace_size = mha_graph.get_workspace_size();
|
||||
auto workspace_ptr =
|
||||
|
||||
@ -30,7 +30,7 @@ static const std::unordered_map<
|
||||
};
|
||||
|
||||
|
||||
// This is the heuristic to choose a kernel based on inputs
|
||||
// This is the heursitic to choose a kernel based on inputs
|
||||
BGEMMKernel_BFloat16 dispatch_bfloat16_bgemm(CUDABLAS_BGEMM_ARGTYPES(at::BFloat16)) {
|
||||
// Optional/future use: directly lookup shape tuples to map to instances
|
||||
/*
|
||||
|
||||
@ -11,7 +11,7 @@ using S = ck::Sequence<Is...>;
|
||||
namespace at::native {
|
||||
|
||||
void dispatch_bfloat16_gemm(CUDABLAS_GEMM_ARGTYPES(at::BFloat16)) {
|
||||
// If any of the shapes can't be tiled, we must use padding.
|
||||
// If any of the shapes cant be tiled, we must use padding.
|
||||
bool use_padding = ((m % 256 != 0) || (n % 128 != 0) || (k % 64 != 0));
|
||||
// Dispatch to best implementation.
|
||||
// TODO add more configurations. Optimize.
|
||||
@ -471,7 +471,7 @@ void dispatch_bfloat16_gemm(CUDABLAS_GEMM_ARGTYPES(at::BFloat16)) {
|
||||
}
|
||||
|
||||
void dispatch_bfloat16_gemm_wmma(CUDABLAS_GEMM_ARGTYPES(at::BFloat16)) {
|
||||
// If any of the shapes can't be tiled, we must use padding.
|
||||
// If any of the shapes cant be tiled, we must use padding.
|
||||
bool use_padding = ((m % 256 != 0) || (n % 128 != 0) || (k % 64 != 0));
|
||||
// Dispatch to best implementation.
|
||||
// TODO add more configurations. Optimize.
|
||||
|
||||
@ -11,7 +11,7 @@ using S = ck::Sequence<Is...>;
|
||||
namespace at::native {
|
||||
|
||||
void dispatch_float_gemm(CUDABLAS_GEMM_ARGTYPES(float)) {
|
||||
// If any of the shapes can't be tiled, we must use padding.
|
||||
// If any of the shapes cant be tiled, we must use padding.
|
||||
bool use_padding = ((m % 256 != 0) || (n % 128 != 0) || (k % 64 != 0));
|
||||
// Dispatch to best implementation.
|
||||
// TODO add more configurations. Optimize.
|
||||
|
||||
@ -13,7 +13,7 @@ namespace at::native {
|
||||
|
||||
void dispatch_half_gemm(CUDABLAS_GEMM_ARGTYPES(at::Half)) {
|
||||
#if 0
|
||||
// If any of the shapes can't be tiled, we must use padding.
|
||||
// If any of the shapes cant be tiled, we must use padding.
|
||||
bool use_padding = ((m % 256 != 0) || (n % 128 != 0) || (k % 64 != 0));
|
||||
// Dispatch to best implementation.
|
||||
// TODO add more configurations. Optimize.
|
||||
@ -299,7 +299,7 @@ void dispatch_half_gemm(CUDABLAS_GEMM_ARGTYPES(at::Half)) {
|
||||
#endif
|
||||
}
|
||||
void dispatch_half_gemm_wmma(CUDABLAS_GEMM_ARGTYPES(at::Half)) {
|
||||
// If any of the shapes can't be tiled, we must use padding.
|
||||
// If any of the shapes cant be tiled, we must use padding.
|
||||
bool use_padding = ((m % 256 != 0) || (n % 128 != 0) || (k % 64 != 0));
|
||||
// Dispatch to best implementation.
|
||||
// TODO add more configurations. Optimize.
|
||||
|
||||
@ -545,7 +545,7 @@ kernel void reshape(texture2d_array<half, access::read> in_arr[[texture(0), func
|
||||
const ushort slices2 = divRoundUp(C2, 4);
|
||||
const ushort slices1 = divRoundUp(C1, 4);
|
||||
const ushort n2 = gid.z / slices2; //image index
|
||||
const ushort s2 = gid.z - n2 * slices2; // slice offset
|
||||
const ushort s2 = gid.z - n2 * slices2; // slice offest
|
||||
half4 value;
|
||||
for (int idx = 0; idx < 4; ++idx){
|
||||
// we compute the "linear index" of the output element,
|
||||
|
||||
@ -86,4 +86,4 @@ TORCH_LIBRARY_IMPL(aten, Metal, m) {
|
||||
m.impl(TORCH_SELECTIVE_NAME("aten::hardsigmoid_"), TORCH_FN(hardsigmoid_));
|
||||
}
|
||||
|
||||
} // namespace at::native::metal
|
||||
} // namepsace at::native::metal
|
||||
|
||||
@ -34,7 +34,7 @@ namespace at::native::onednn {
|
||||
|
||||
/*
|
||||
oneDNN postops usage:
|
||||
Currently, oneDNN supports 5 kinds of post ops. More details can be referred
|
||||
Currently, oneDNN supports 5 kinds of post ops. More details can be refered
|
||||
to oneDNN doc.
|
||||
https://oneapi-src.github.io/oneDNN/dev_guide_attributes_post_ops.html#doxid-dev-guide-attributes-post-ops-1dev-guide-attributes-post-ops-eltwise
|
||||
|
||||
@ -399,7 +399,7 @@ static inline void construct_attr_for_unary(
|
||||
} else {
|
||||
TORCH_CHECK(
|
||||
unary_post_op == "none",
|
||||
"onednn qlinear: unsupported unary post op",
|
||||
"onednn qlinear: unspported unary post op",
|
||||
unary_post_op);
|
||||
}
|
||||
}
|
||||
|
||||
@ -856,7 +856,7 @@ id<MTLLibrary> MetalShaderLibrary::getLibrary(const std::initializer_list<std::s
|
||||
break;
|
||||
}
|
||||
default:
|
||||
TORCH_INTERNAL_ASSERT(false, "Unsupported number of parameters ", nparams);
|
||||
TORCH_INTERNAL_ASSERT(false, "Unsupported number of paramaters ", nparams);
|
||||
}
|
||||
return libMap[key] = lib;
|
||||
}
|
||||
@ -1184,9 +1184,9 @@ void MetalKernelFunction::dispatch(uint64_t length, std::optional<uint64_t> grou
|
||||
}
|
||||
|
||||
void MetalKernelFunction::dispatch(c10::ArrayRef<uint64_t> length, c10::OptionalArrayRef<uint64_t> group_size) {
|
||||
TORCH_CHECK(!length.empty() && length.size() < 4, "Dispatch dimensions must be less than 3 and non-empty");
|
||||
TORCH_CHECK(!length.empty() && length.size() < 4, "Dispatch dimentions must be less than 3 and non-empty");
|
||||
TORCH_CHECK(!group_size.has_value() || group_size->size() == length.size(),
|
||||
"size and group_size must have same number of dimensions");
|
||||
"size and group_size must have same number of dimentions");
|
||||
const auto max_tg_size = getMaxThreadsPerThreadgroup();
|
||||
const auto group_size_length = group_size.has_value() ? group_size->size() : 0;
|
||||
auto tg_size = MTLSizeMake(group_size_length > 0 ? group_size->at(0) : max_tg_size,
|
||||
|
||||
@ -59,7 +59,7 @@ static GridSamplerOffsets find_grid_sampler_offsets(
|
||||
return offsets;
|
||||
}
|
||||
|
||||
// Mod function which gives positive output when `a` is negative
|
||||
// Mod function which gives postive output when `a` is negative
|
||||
static int32_t mod(int32_t a, int32_t b) {
|
||||
auto r = a % b;
|
||||
return r + (r < 0 ? b : 0);
|
||||
@ -191,9 +191,9 @@ void grid_sampler_single_element(
|
||||
int32_t right_indices[3];
|
||||
opmath_t<T> scales[3];
|
||||
|
||||
// For each dimension, find the pair of indices in the corresponding dimension
|
||||
// For each dimension, find the pair of indices in the cooresponding dimension
|
||||
// of `input` which surround the grid coordinate in that dimension. We'll do
|
||||
// this by mapping different coordinate spaces onto each other. There are
|
||||
// this by mapping different coordiante spaces onto each other. There are
|
||||
// basically three different coordinate spaces to keep in mind:
|
||||
//
|
||||
// * aligned grid space
|
||||
|
||||
@ -137,7 +137,7 @@ kernel void index_put_serial(
|
||||
constant int64_t* index_strides,
|
||||
constant uint4& ndim_nindices_numel,
|
||||
uint thread_index [[thread_position_in_grid]]) {
|
||||
(void)thread_index; // Suppress unused variable warning
|
||||
(void)thread_index; // Suppress unused vairable varning
|
||||
for (uint idx = 0; idx < ndim_nindices_numel.z; ++idx) {
|
||||
index_put_impl(
|
||||
output,
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user